Jump to content

Patent Application 18712260 - COMMUNICATION NETWORK NODE METHOD COMMUNICATION - Rejection

From WikiPatents

Patent Application 18712260 - COMMUNICATION NETWORK NODE METHOD COMMUNICATION

Title: COMMUNICATION NETWORK NODE, METHOD, COMMUNICATION NETWORK, TERMINAL DEVICE

Application Information

  • Invention Title: COMMUNICATION NETWORK NODE, METHOD, COMMUNICATION NETWORK, TERMINAL DEVICE
  • Application Number: 18712260
  • Submission Date: 2025-05-13T00:00:00.000Z
  • Effective Filing Date: 2024-05-22T00:00:00.000Z
  • Filing Date: 2024-05-22T00:00:00.000Z
  • Examiner Employee Number: 94795
  • Art Unit: 3628
  • Tech Center: 3600

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 5

Cited Patents

No patents were cited in this rejection.

Office Action Text


    DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 4/11/2025.
Claims 1, 3, and 43-44 have been amended and are hereby entered.
Claim 2 has been canceled.
Claims 1, 3-16, 18-19, and 43-44 are currently pending and have been examined.
This action is made FINAL.
Domestic Benefit/International Priority 
The ADS filed on 5/22/2024 claims ‘371 benefit of PCT/EP2022/085999 (filed 12/14/2022) and priority to EP 21215734.1 (filed 12/17/2021).  All claims as presently drafted are fully supported by these references; therefore, all claims as presently drafted are granted an effective filing date of 12/17/2021.
Response to Applicant’s Arguments
Objections
	The present amendment to the abstract obviates the previous objection thereto; therefore, this objection is withdrawn.


Claim Rejections – 35 USC § 101
	Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
	Applicant’s only substantive argument is an assertion of an improvement to a technology under Step 2A, Prong Two.  Particularly, Applicant argues that “the amended claim recites a specific implementation that improves the technical field of mobility service systems by integrating neural network technology with smart city infrastructure data. This technological integration is not merely a generic application of computing technology but a specific technical architecture that creates a new and improved mobility service system. As detailed in paragraphs [0059]-[0065] of the specification, this approach solves the technical problem of providing future experience estimations rather than relying solely on historical data. In other words, the amended claim is directed to solving specific technological problems in mobility service systems as articulated in paragraphs [0066]-[0068] of the specification namely, the technical challenge of providing customer-centric rather than operator-oriented service by predicting and adapting to future conditions rather than simply reacting to past data.”  Examiner disagrees.
	Examiner finds that the improvements asserted by Applicant are not improvements to technology (which would qualify as eligible subject matter), but rather broader improvements to abstract concepts which Applicant merely couches in technological language.  As per MPEP 2106.05(a), such an improvement to technology is “a technological solution to a technological problem.”  As further explained in the same section, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”  Applicant’s argued improvement neither provides a technological solution nor is directed to a technological problem.  
	Firstly, as explained in the Interview of 3/20/2025, mobility as a service is not a technology, but rather an abstract business concept.  Secondly, the asserted “integrating [of] neural network technology with smart city infrastructure data” certainly is “merely a generic application of computing technology” as presently claimed.  Specifically, the function of the neural network as claimed is purely abstract (ie: receiving quantitative and qualitative data, and using it in a determinative model to output a predicted user experience).  In any neural network (or more broadly, determinative model), input data must be provided.  Merely claiming that such a model receive particular abstract pieces of data (ie: “real-time information about traffic conditions and road congestion” as claimed) from a separate computer element (ie: a smart city management system) does not provide any improvement to a technology. Relatedly, the use of such particular abstract pieces of data in a model to perform an abstract determination (a predicted user experience) is a purely abstract improvement, not a technological one.  Claiming the model itself to be in the form of a neural network, and even further vaguely claiming different “layers” of such a neural network, does not make this otherwise.  Rather, claimed as these computer elements are at a high level as performing abstract functions, these represent mere instructions to apply an additional element (ie: using a computer as a tool to perform a judicial exception) as per MPEP 2106.05(f).  Nothing in the specification, either in the cited paragraphs or otherwise, supports the notion that any technology itself is actually improved by these amended features (see, e.g., Paragraphs 0097, merely indicating that various outside computer elements, including the smart city management system, are queried for various pieces of abstract input data used in the abstract prediction of a user experience).  
	Further, the asserted “providing future experience estimations rather than relying solely on historical data” and “providing customer-centric rather than operator-oriented service by predicting and adapting to future conditions rather than simply reacting to past data” are not “technical problems/challenges” as argued, but rather purely abstract solutions to purely abstract, business-related problems.  There is nothing inherently technological about predicting future customer experiences and using said predictions to choose/rank a mobiliy-as-a-service type route to offer or provide to said user.  This is particularly clear as each and every step (e.g., receiving various abstract pieces of input data, feeding them into a deterministic model to predict a user experience, ranking potential MaaS routes based on user experience, etc.) recited in the independent claims could be performed manually, absent any of the recited additional elements (e.g., the neural network and the smart city management system) to achieve the same results.  That doing so via computer elements such as the claimed neural network might make these abstract determinations faster or more efficiently performed does not make this otherwise (see, e.g., Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)).  Referring to this mere computer automation of an abstract process as a “technical integration,” or mobility-as-a-service as a “technical field” does not make it so.  
	Examiner also notes the following assertion by Applicant: “Consideration of improvements is relevant to the eligibility analysis regardless of the technology of the claimed invention. See, e.g. Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016)” (Applicant’s emphasis).  As best as Examiner can determine based on this unexplained conclusory statement, particularly as used in relation to the arguments addressed above, Applicant misapprehends and mischaracterizes the content of Rapid Litigation Management.  Nothing in this holding disconnects an improvement rendering the claims subject matter eligible from technology.  Rather, this holding indicates (in relevant part), that such an improvement to a technology renders a claim subject matter eligible regardless of the particular technology (ie: the type or realm of technology) being improved.  However, as noted above in relation to MPEP 2106.05(a), the improvement must still be an improvement to a technology of some kind, as “technology” is understood within the 101 subject matter eligibility analysis, to be subject matter eligible under this standard.  As discussed above, the improvement asserted by applicant fails to qualify as such, instead being an improvement to an abstract idea which happens to be claimed as being performed by and via computer elements.  
Claim Rejections – 35 USC § 102/103
	Applicant’s arguments regarding the 102 and 103 analyses have been considered and are unpersuasive.
	Applicant’s arguments against the previously cited references are purely based on newly drafted language, and as such need not be addressed here.  These arguments are moot in view of the updated 103 rejections below.
Claim Rejections – 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.

The following is a quotation of the first paragraph of pre-AIA  35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.

Claims 1, 3-16, 18-19, and 43-44 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA  35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. 
Claims 1, 43, and 44 contain the following limitation: “wherein the neural network quantifies qualitative factors by transforming qualitative inputs into numerical inputs.”  This limitation is not properly supported by the original disclosure, and as such constitutes new matter.  Particularly, Paragraph 0165 discloses the following: “The input data for the machine learning algorithm may be pre-processed, for example qualitative factors may be transformed into quantities (i.e., the qualitative factor may be quantified, as discussed above), such that a numerical input may be available for the machine learning algorithm.”  This differs from the quoted claim language in that, while this quantified qualitative data is fed into the ML algorithm (or, as claimed, the neural network), it is not the neural network itself which performs this quantification of the qualitative factor.  Rather, the qualitative factors are “pre-processed,” ie: are quantified prior to being provided to the neural network as input data.  This is further borne out in Paragraphs 0166-0178, wherein this quantification is performed based on a predetermined set of rules (e.g., Tables 1 and 2) rather than by the machine learning algorithm/neural network.  There is no other disclosure in the specification which treats this quantified qualitative data as anything other than input data, rather than an output of the neural network as presently claimed.  As such, this limitation is not supported.  Claims 3-16 and 18-19 are rejected due to their dependence upon Claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.


The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.


Claims 1, 3-16, 18-19, and 43-44 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 43 contain the following limitation: “provide the distributed ledger based on blockchain technology for sharing travel transaction data between multiple mobility service providers.”  In this limitation, the term “the distributed ledger” lacks antecedent basis, as no distributed ledger is previously disclosed in these claims.  For the purposes of this examination, the term “the distributed ledger” will be interpreted as “a distributed ledger.”  Claims 3-16 and 18-19 are rejected due to their dependence upon Claim 1.
Claims 1, 43, and 44 contain variations on the following limitations: “interface with a smart city management system to obtain real-time information about traffic conditions and road congestion” and “adapt the ranked MaaS travel routes based on dynamic traffic information received from the smart city management system.”  Due to the inconsistent but seemingly identical in meaning terminology, it is unclear as drafted whether the “real-time information about traffic conditions and road congestion” and “dynamic traffic information” of these respective limitations, both received from the smart city management system, indicate the same or different things.  For the purposes of this examination, these terms will be interpreted as if indicating the same thing.  Claims 3-16 and 18-19 are rejected due to their dependence upon Claim 1.
Claims 1, 43, and 44 contain variations on the following limitation: “predict a user experience based on a deterministic model including the at least one service factor.”  Claims 1, 43, and 44 also contain the following additional terms: “a predicted user experience value,” “the predicted future user experience,” and “the future user experience.”  It is unclear as drafted whether any of these disparate terms are intended to relate back to the “user experience” predicted in the quoted limitation above.  For the purposes of this examination, each of these disparate terms are interpreted as “the predicted user experience.”  Claims 3-16 and 18-19 are rejected due to their dependence upon Claim 1.
Claim 3 contains the term “the quantitative factor,” which lacks antecedent basis due to the present cancellation of Claim 2 (upon which Claim 3 previously depended).  Examiner notes that it is unclear as drafted whether this term (singular) is intended to relate back to the new term “quantitative and qualitative service factors” (plural) of Claim 1.  For the purposes of this examination, this term will be interpreted as “a quantitative factor of the at least one service factor.”  
Claim 18 contains the following limitation: “quantifying a qualitative factor of the at least one service factor based on the user input.”  It is unclear as drafted whether this is intended to relate back to/indicate the same limitation as “wherein the neural network quantifies qualitative factors by transforming qualitative inputs into numerical inputs” of Claim 1 (upon which Claim 18 depends).  For the purposes of this examination, these limitations will be interpreted as indicating the same step.  
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.


Claims 1-16, 18-19, and 43-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claims 1 and 43, the limitations of acquire user input which is indicative of at least one service factor indicating user experience; predict a user experience based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; rank multiple mobility as a service (MaaS) travel routes based on the predicted future user experience; interface with an outside source to obtain real-time information about traffic conditions and road congestion; incorporate the real-time information from the smart city management system into the deterministic model for predicting the future user experience; adapt the ranked MaaS travel routes based on dynamic traffic information received from the smart city management system; and providing/sharing travel transaction data between multiple mobility service providers, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity.  For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).  
Additionally, the limitations of acquire user input which is indicative of at least one service factor indicating user experience; predict a user experience based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; rank multiple mobility as a service (MaaS) travel routes based on the predicted future user experience; interface with an outside source to obtain real-time information about traffic conditions and road congestion; incorporate the real-time information from the smart city management system into the deterministic model for predicting the future user experience; adapt the ranked MaaS travel routes based on dynamic traffic information received from the smart city management system; and providing/sharing travel transaction data between multiple mobility service providers, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes.  For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).  
Additionally, the limitations of predict a user experience based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; rank multiple mobility as a service (MaaS) travel routes based on the predicted future user experience; interface with an outside source to obtain real-time information about traffic conditions and road congestion; incorporate the real-time information from the smart city management system into the deterministic model for predicting the future user experience; and adapt the ranked MaaS travel routes based on dynamic traffic information received from the smart city management system, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts.  For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.  If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas.  If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas.  Accordingly, the claims recite an abstract idea.
	The judicial exception is not integrated into a practical application.  In particular, the claim recites the additional elements of a communication network comprising a communication node, said communication node comprising circuitry; a neural network having an input layer, multiple intermediate layers, and an output layer; a smart city management system; and a distributed ledger based on blockchain technology.  These amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)).  Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas.  The claims are therefore directed to an abstract idea.
	The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.  As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception for the same reasons as discussed above in relation to integration into a practical application.  These cannot provide an inventive concept.  Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.  
Claims 3-16 and 18-19, describing various additional limitations to the system of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.  
Claim 3 discloses wherein the quantitative factor includes at least one of on-board time, waiting-time, waiting-time for transition, a number of changes, a number of persons at a station, a number of persons inside a train, transport costs, punctuality, unexpected delay, and a number of complaints to a transport service (further defining the abstract idea already set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 4 discloses wherein the at least one service factor includes a qualitative factor (further defining the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 5 discloses wherein the qualitative factor includes at least one of comfort options, purpose of travel, additional travel services, accessibility, easiness of luggage handling, familiarity of the user with the transport service, familiarity of the user with the destination, risk of traffic accident, security, public safety, public health, and user health (further defining the abstract idea already set forth in Claim 4), which does not integrate the claim into a practical application.
Claim 6 discloses acquire a measurement of the at least one service factor (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 7 discloses wherein the measurement is carried out by at least one of a railway control system, a smartphone, and a camera (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 8 discloses carry out a deterministic calculation of the at least one service factor (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 9 discloses wherein the deterministic calculation includes calculating at least one of an on-board time, a waiting time, and a number of changes (further defining the abstract idea already set forth in Claim 8), which does not integrate the claim into a practical application.
Claim 10 discloses wherein the deterministic calculation is indicative of at least one of a preferable transport and a journey time (further defining the abstract idea already set forth in Claim 8), which does not integrate the claim into a practical application.
Claim 11 discloses wherein the deterministic calculation includes regression (further defining the abstract idea already set forth in Claim 8), which does not integrate the claim into a practical application.
Claim 12 discloses wherein the regression includes multiple pattern regression (further defining the abstract idea already set forth in Claim 11), which does not integrate the claim into a practical application.
Claim 13 discloses acquire a travel history of the user (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 14 discloses prioritize a travel route based on the travel history (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 15 discloses wherein the deterministic model includes a machine learning algorithm (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 16 discloses wherein the machine learning algorithm is based on at least one input variable (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 18 discloses quantifying a qualitative factor of the at least one service factor based on the user input (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 19 discloses in the case of more than one input variable: reducing the at least one input variable based on a dimension reduction technique (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Regarding Claim 44, the limitations of provide a user input which is indicative of at least one service factor indicating user experience; and obtain a user experience which is predicted based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; receive multiple mobility as a service (MaaS) travel routes ranked based on the predicted future user experience; and wherein the ranked MaaS travel routes are adapted based on dynamic traffic information received from an outside source that incorporates real-time information into the deterministic model for predicting the future user experience, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity.  For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).  
Additionally, the limitations of provide a user input which is indicative of at least one service factor indicating user experience; and obtain a user experience which is predicted based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; receive multiple mobility as a service (MaaS) travel routes ranked based on the predicted future user experience; and wherein the ranked MaaS travel routes are adapted based on dynamic traffic information received from an outside source that incorporates real-time information into the deterministic model for predicting the future user experience, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes.  For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).  
Additionally, the limitations of obtain a user experience which is predicted based on a deterministic model including the at least one service factor; wherein the deterministic model receives quantitative and qualitative service factors and provides a predicted user experience value; quantifying qualitative factors by transforming qualitative inputs into numerical inputs; receive multiple mobility as a service (MaaS) travel routes ranked based on the predicted future user experience; and wherein the ranked MaaS travel routes are adapted based on dynamic traffic information received from an outside source that incorporates real-time information into the deterministic model for predicting the future user experience, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts.  For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.  If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas.  If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas.  Accordingly, the claim recites an abstract idea.
	The judicial exception is not integrated into a practical application.  In particular, the claim recites the additional elements of a terminal device configured to communicate with a communication network node in a communication network; a neural network having an input layer, multiple intermediate layers, and an output layer; and a smart city management system.  These amounts to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)).  Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas.  The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.  As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception for the same reasons as discussed above in relation to integration into a practical application.  These cannot provide an inventive concept.  Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.  
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.  
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-5, 8-10, 13, 15-16, 18, and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Goldstein et al (PGPub 20200250196) (hereafter, “Goldstein”) in view of Chen et al (PGPub 20170364933) (hereafter, “Chen”), Shoshan et al (WO 2020075164) (hereafter, “Shoshan”), and Han et al (KR 20200094985) (hereafter, “Han”).  
	Regarding Claims 1 and 43, Goldstein discloses: 
A communication network comprising a communication network node including circuitry (Abstract; ¶ 0007-0010, 0021-0022, 0065-0069; Figs. 1-2, 5; computing system which can predict negative experiences of users; the communication system can communicate, over one or more networks, with the service application executing on the user computing devices and the driver application executing on driver computing devices of the transport providers); 
acquire user input which is indicative of at least one service factor indicating user experience (Abstract; ¶ 0007-0008, 0023-0026, 0056, 0059; Figs. 3-4; a service entity can coordinate or manage the application service via backend computing systems (e.g., a remote data center), that receive various event data from the computing devices of the users; the event data can comprise user input data corresponding to user inputs on an application interface generated on a display screen of the user's computing device; the event data can further include sensor data and/or location data from sensor and/or positioning system resources on the computing devices of the users; the event data can comprise third party data received from third party sources, such as media sources, mapping and/or traffic modeling sources, scheduling or calendar sources, and the like; computing system of the service entity can ingest the event data and generate service representations that correspond to a particular user's experience with the application service); 
predict a user experience based on a deterministic model including the at least one service factor (Abstract; ¶ 0008-0010, 0027-0028, 0057-0058, 0060-0061; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider); 
wherein the neural network quantifies qualitative factors by transforming qualitative inputs into numerical inputs (¶ 0027-0032, 0052-0053; the user profile for a user can indicate how many transport requests the user has submitted, an average user rating as rated by matched transport providers, etc.; the engagement level of a user or transport provider can comprise an total amount or rate of usage of the network service, and can correspond to an actual monetary figure, a monetary rate (e.g., dollar value over time), or an abstract value (e.g., a points value) or the user or transport provider based on, for example, an average rating, feedback from matched users or transport providers, money spent using the network service, etc.).
Goldstein does not explicitly disclose but Chen does disclose wherein the deterministic model comprises a neural network having an input layer receiving inputs, multiple intermediate layers, and an output layer providing a predicted user experience value (¶ 0106-0109, 0120; Figs. 9A-9B; the variables that may determine whether the user will be lost may be determined as output variables of the predictive model; if the predictive model is a model based on the neural network algorithm (also referred to herein as a “neural network module”), the adjusting of the predictive model according to the error may include adjusting at least one of the number of the input variables of the neural network model, the number of hidden layers, the number of neurons in the hidden layers, the transfer function of the hidden layers, and the transfer function of the output layer. The adjusting of the transfer function of the hidden layers may further include adjusting the weight coefficients of the neurons).  Goldstein additionally discloses wherein the inputs include quantitative and qualitative service factors; wherein the deterministic model provides a predicted user experience value (Abstract; ¶ 0008-0010, 0024-0029, 0057-0058, 0060-0061; Figs. 3-4; a current representation for a user can indicate the status of the user, such as whether the user is on-app (e.g., currently interacting with the executing application), off-app (e.g., has deactivated the application), and the user's current state while the application is executing; the service representation can further include contextual information, such as a session time for the user interacting with the service application, a wait time for transport, changing ETAs of a matched transport provider, the transport provider's route and/or navigation information, the actual marketplace conditions, current pricing data for the network service, etc.; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider).
Goldstein does not explicitly disclose but Shoshan does disclose rank multiple mobility as a service (MaaS) travel routes based on a predicted user experience (pgs. 3, 23-24, 37-39; certain embodiments seek to provide a MaaS system that may define and manage QoS of each ride request; the proposal pool is the set of available and valid sendee plans alternatives in which the request metric is met, and may be the full list of alternative service plans made by an exhaustive search over all possible routes; this proposal pool is further filtered, for example, by using the user/passenger model which learns the user preferences with regard to his/her rides; the proposals that are part of the proposals pool may be ranked/prioritized for filtering; the proposals pool proposals are arranged according to a given rank/metric of the proposal; the rank may be any combination/function of the expected user experience and the price of the proposal).  Goldstein additionally discloses wherein a predicted user experience is the predicted future user experience (Abstract; ¶ 0008-0010, 0027-0028, 0057-0058, 0060-0061; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider).
Goldstein additionally discloses interface with a third party source to obtain real-time information about traffic conditions and road congestion (¶ 0007, 0026, 0057, 0059; event data/contextual information may include traffic conditions, which may further be third party data received from third party sources).  Goldstein does not explicitly disclose but Shoshan does disclose wherein the third party source is a smart city management system (pg. 10; the QMSC may also be connected to various external data sources to get access to data that may improve the system performance; examples of such external data sources may be any road traffic congestion data source, such as road speed sensors, road cameras, etc.).
Goldstein additionally discloses incorporate the real-time information from the third party source into the deterministic model for predicting the future user experience (Abstract; ¶ 0007-0008, 0026, 0030, 0032, 0056-0057, 0059-0060; Figs. 3-4;  using the event data, the computing system can generate representations corresponding to user experience in connection with the transport; based on the representations, the computing system can predict whether a negative user experience will occur for either the user or the transport provider at a future instance in time or over a given time interval; for example, the computing system can execute an artificial intelligence model that processes the representations to make a future prediction of whether any particular user or transport provider in the given region will have a negative user experience at a future instance; said event data may include traffic conditions, which may further be third party data received from third party sources).  Goldstein does not explicitly disclose but Shoshan does disclose wherein the third party source is a smart city management system (pg. 10; the QMSC may also be connected to various external data sources to get access to data that may improve the system performance; examples of such external data sources may be any road traffic congestion data source, such as road speed sensors, road cameras, etc.).
Goldstein does not explicitly disclose but Shoshan does disclose adapt the ranked MaaS travel routes based on user experience; wherein dynamic traffic information is received from the smart city management system (pgs. 3, 10, 23-24, 37-39; the QMSC may also be connected to various external data sources to get access to data that may improve the system performance; examples of such external data sources may be any road traffic congestion data source, such as road speed sensors, road cameras, etc.; certain embodiments seek to provide a MaaS system that may define and manage QoS of each ride request; the proposal pool is the set of available and valid sendee plans alternatives in which the request metric is met, and may be the full list of alternative service plans made by an exhaustive search over all possible routes; this proposal pool is further filtered, for example, by using the user/passenger model which learns the user preferences with regard to his/her rides; the proposals that are part of the proposals pool may be ranked/prioritized for filtering; the proposals pool proposals are arranged according to a given rank/metric of the proposal; the rank may be any combination/function of the expected user experience and the price of the proposal).  Goldstein additionally discloses wherein user experience is determined based on dynamic traffic information (Abstract; ¶ 0007-0008, 0026, 0030, 0032, 0056-0057, 0059-0060; Figs. 3-4;  using the event data, the computing system can generate representations corresponding to user experience in connection with the transport; based on the representations, the computing system can predict whether a negative user experience will occur for either the user or the transport provider at a future instance in time or over a given time interval; for example, the computing system can execute an artificial intelligence model that processes the representations to make a future prediction of whether any particular user or transport provider in the given region will have a negative user experience at a future instance; said event data may include traffic conditions, which may further be third party data received from third party sources).
Goldstein does not explicitly disclose but Han does disclose provide the distributed ledger based on blockchain technology for sharing travel transaction data between multiple mobility service providers (Abstract; pgs. 3-4, 7; Claim 9; a communication unit configured to transmit a data block to the node through a block chain network consisting of nodes, each of a plurality of participating companies providing vehicle-related services).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the machine learning model-based prediction techniques of Chen with the transportation service data analysis and experience prediction system of Goldstein because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143).  The known techniques of Chen are applicable to the base device (Goldstein), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.  One of ordinary skill in the art would further have been motivated to include the transportation service data intake, prediction, and analysis techniques of Shoshan with the transportation service data analysis and experience prediction system of Goldstein and Chen to improve planning, operation, dynamic allocation, and routing of vehicles in intelligent transportation systems, such as that of Goldstein (see at least pgs. 4 and 12 of Shoshan).  It further would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the transportation service-based communication structure and techniques of Han with the transportation service data analysis and experience prediction system of Goldstein, Chen, and Shoshan because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143).  The known techniques of Han are applicable to the base device (Goldstein, Chen, and Shoshan), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 3, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses wherein the quantitative factor includes at least one of on-board time, waiting-time, waiting-time for transition, a number of changes, a number of persons at a station, a number of persons inside a train, transport costs, punctuality, unexpected delay, and a number of complaints to a transport service (¶ 0009-0010, 0024-0026; the service representation can further include contextual information, such as a session time for the user interacting with the service application, a wait time for transport, changing ETAs of a matched transport provider, the transport provider's route and/or navigation information, the actual marketplace conditions, current pricing data for the network service, etc.).  
Regarding Claim 4, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses wherein the at least one service factor includes a qualitative factor (¶ 0009-0010, 0025-0029; a current representation for a user can indicate the status of the user, such as whether the user is on-app (e.g., currently interacting with the executing application), off-app (e.g., has deactivated the application), and the user's current state while the application is executing; the service representations can further account for the historical utilization of the network service by a matched transport provider (e.g., whether the transport provider has a history of good performance or a propensity towards poor performance); the service representation can indicate a reason for an increasing or stagnant ETA (e.g., unexpected traffic, the transport provider making a wrong turn or staying stationary, the transport provider not following a route trajectory, etc.); the representation can provide further contextual information, such as the nature of a pick-up location (e.g., an airport, home location, business location, etc.), the destination location, requested goods (e.g., comestible items), and the like; the service representations can be individual to the user based on historical utilization data corresponding to the user's historical usage of the network service (e.g., stored in a user profile of the user)).  
Regarding Claim 5, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 4.  Goldstein additionally discloses wherein the qualitative factor includes at least one of comfort options, purpose of travel, additional travel services, accessibility, easiness of luggage handling, familiarity of the user with the transport service, familiarity of the user with the destination, risk of traffic accident, security, public safety, public health, and user health (¶ 0009-0010, 0025-0029; the service representations can be individual to the user based on historical utilization data corresponding to the user's historical usage of the network service (e.g., stored in a user profile of the user)).  
Regarding Claim 8, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses carry out a deterministic calculation of the at least one service factor (¶ 0009, 0011-0013, 0025-0026; estimated times of arrival (ETAs) of proximate drivers, and changes therein; a distance or time to the transport provider's home location).  
Regarding Claim 9, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 8.  Goldstein additionally discloses wherein the deterministic calculation includes calculating at least one of an on-board time, a waiting time, and a number of changes (¶ 0009-0010, 0024-0026; the service representation can further include contextual information, such as a session time for the user interacting with the service application, a wait time for transport, changing ETAs of a matched transport provider, the transport provider's route and/or navigation information, the actual marketplace conditions, current pricing data for the network service, etc.).
Regarding Claim 10, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 8.  Goldstein additionally discloses wherein the deterministic calculation is indicative of at least one of a preferable transport and a journey time (¶ 0009, 0025-0026; estimated times of arrival (ETAs) of proximate drivers, and changes therein; a distance or time to the transport provider's home location).
Regarding Claim 13, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses acquire a travel history of the user (¶ 0009, 0028; the service representations can be individual to the user based on historical utilization data corresponding to the user's historical usage of the network service (e.g., stored in a user profile of the user)).
Regarding Claim 15, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses wherein the deterministic model includes a machine learning algorithm (Abstract; ¶ 0010-0013, 0027, 0060; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can execute an artificial intelligence model that can analyze the service representations in accordance with a set of goals; the artificial intelligence model can employ deep learning techniques to continuously refine negative experience prediction and detection; the computing system can execute an artificial intelligence model that processes the representations to make a future prediction of whether any particular user or transport provider in the given region will have a negative user experience at a future instance).
Regarding Claim 16, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses wherein the machine learning algorithm is based on at least one input variable (Abstract; ¶ 0010-0013, 0027, 0060; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can execute an artificial intelligence model that can analyze the service representations in accordance with a set of goals; the artificial intelligence model can employ deep learning techniques to continuously refine negative experience prediction and detection; the computing system can execute an artificial intelligence model that processes the representations to make a future prediction of whether any particular user or transport provider in the given region will have a negative user experience at a future instance).
Regarding Claim 18, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 1.  Goldstein additionally discloses quantifying a qualitative factor of the at least one service factor based on the user input (¶ 0028-0032, 0052-0053; the user profile for a user can indicate how many transport requests the user has submitted, an average user rating as rated by matched transport providers, etc.; the engagement level of a user or transport provider can comprise an total amount or rate of usage of the network service, and can correspond to an actual monetary figure, a monetary rate (e.g., dollar value over time), or an abstract value (e.g., a points value) or the user or transport provider based on, for example, an average rating, feedback from matched users or transport providers, money spent using the network service, etc.).  
Claims 6-7 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Goldstein in view of Chen, Shoshan, Han, and Poornachandran et al (PGPub 20180089605) (hereafter, “Poornachandran”).
	Regarding Claim 6, Goldstein in view of Chen, Shoshan, and Handiscloses the limitations of Claim 1.  Goldstein does not explicitly disclose but Poornachandran does disclose acquire a measurement of the at least one service factor (¶ 0016, 0037-0038, 0058, 0086, 0098; input sources may include Internet of Things (TOT) sensing devices, cameras, microphones, user wearable devices, GPS devices, smartphones, tablets, laptops, desktops, and other sensors in a position to monitor the driver user or passenger user; context determination and inference module may receive context events from data aggregation module from the passenger user and/or from the driver users; context event information may include video information from a video camera of a computing device (e.g., a video camera, a 3D camera, a sequences of images from the camera which may include 3D Depth map for better emotional characterization, and the like), information from a microphone of the computing device, information from an accelerometer of the computing device, information from wearable sensors, information from vehicle sensors, and the like; video and audio may be utilized to determine one or more emotions of the users).  
The rationale to combine Goldstein, Chen, and Shoshan remains the same as for Claim 1.  One of ordinary skill in the art would have been motivated to include the contextual data gathering and experience prediction functionalities of Poornachandran with the service data analysis and experience prediction system of Goldstein, Chen, Shoshan, and Han to provide for improved matching of drivers and passengers in ride sharing systems using automatically determined user contexts (see at least the Abstract and Paragraph 0012 of Poornachandran).  
Regarding Claim 7, Goldstein in view of Chen, Shoshan, Han, and Poornachandran discloses the limitations of Claim 6.  Goldstein does not explicitly disclose but Poornachandran does disclose wherein the measurement is carried out by at least one of a railway control system, a smartphone, and a camera (¶ 0016, 0037-0038, 0058, 0086, 0098; input sources may include Internet of Things (TOT) sensing devices, cameras, microphones, user wearable devices, GPS devices, smartphones, tablets, laptops, desktops, and other sensors in a position to monitor the driver user or passenger user; context determination and inference module may receive context events from data aggregation module from the passenger user and/or from the driver users; context event information may include video information from a video camera of a computing device (e.g., a video camera, a 3D camera, a sequences of images from the camera which may include 3D Depth map for better emotional characterization, and the like), information from a microphone of the computing device, information from an accelerometer of the computing device, information from wearable sensors, information from vehicle sensors, and the like; video and audio may be utilized to determine one or more emotions of the users).  
	The rationale to combine remains the same as for Claim 6.
Regarding Claim 11, Goldstein in view of Chen, Shoshan, Han, and Poornachandran discloses the limitations of Claim 8.  Goldstein does not explicitly disclose but Poornachandran does disclose wherein the deterministic calculation includes regression (¶ 0024-0025, 0051, 0078; the machine learning algorithm may be selected from among many different potential supervised or unsupervised machine learning algorithms, such as a linear and logistic regression model).
The rationale to combine remains the same as for Claim 6.
	Regarding Claim 12, Goldstein in view of Chen, Shoshan, Han, and Poornachandran discloses the limitations of Claim 11.  Goldstein does not explicitly disclose but Chen does disclose wherein the regression includes multiple pattern regression (¶ 0041-0042, 0097-0101; the system or method of the present disclosure may be applied to different transportation systems; a model for predicting user stability may be determined based on the user information, and may utilize various machine learning algorithms; the causal analysis method may further include a univariate regression method, a multiple regression method, and an input-output method).
The rationale to combine Goldstein and Poornachandran remains the same as for Claim 6.  
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Goldstein in view of Chen, Shoshan, Han, and Jha et al (PGPub 20210118081) (hereafter, “Jha”).
Regarding Claim 14, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 13.  Goldstein does not explicitly disclose but Jha does disclose prioritize a travel route based on the travel history (¶ 0054-0056; MaaS F2LM route planning and orchestration capability allows users to specify acceptable reputation scores as input during planning; a MaaS reputation Score and metadata is defined based on multiple factors, context data, and history of past experiences; a specific transport operator may have a reputation as well as the class of transportation offered, or a per route reputation that involves a mix of transportation forms may be compared based on different mixes; MaaS reputation insights are created using artificial intelligence (AI) and deep learning (DL) algorithms that optimize for higher scores given user interest vectors and the available options in the MaaS supply chain).  
	The rationale to combine Goldstein, Chen, and Shoshan remains the same as for Claim 1.  One of ordinary skill in the art would have been motivated to include the route planning reputation determining functionalities of Jha with the service data analysis and experience prediction system of Goldstein, Chen, Shoshan, and Han to consider and apply user trust or confidence in terms of various forms of MaaS service offerings (see at least Paragraph 0054 of Jha).  
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Goldstein in view of Chen, Shoshan, Han, and Otillar et al (PGPub 20180276573) (hereafter, “Otillar”).
	Regarding Claim 19, Goldstein in view of Chen, Shoshan, and Han discloses the limitations of Claim 15.  Goldstein does not explicitly disclose but Otillar does disclose in the case of more than one input variable: reducing the at least one input variable based on a dimension reduction technique (¶ 0047, 0053-0061; machine learning models may be trained to make various predictions about the travelling user; dimensionality reduction (e.g., via linear discriminant analysis, principle component analysis, etc.) can be used to reduce the amount of data in the feature vector to a smaller, more representative core set of features).  
The rationale to combine Goldstein, Chen, and Shoshan remains the same as for Claim 1.  It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the travel-based machine learning techniques of Otillar with the service data analysis and experience prediction system of Goldstein, Chen, Shoshan, and Han because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143).  The known techniques of Otillar are applicable to the base device (Goldstein, Chen, Shoshan, and Han), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Goldstein in view of Chen and Shoshan.
Regarding Claim 44, Goldstein discloses:
A terminal device being configured to communicate with a communication network node in a communication network (Abstract; ¶ 0007-0010, 0048-0052; Figs. 1-2; computing device utilized by users of an application service (e.g., requesting users and transport providers); the computing device can comprise a mobile computing device, such as a smartphone, tablet computer, laptop computer, etc.; the service application of the computing device can further enable a communication like with a network computing system over the network); 
provide a user input which is indicative of at least one service factor indicating user experience (Abstract; ¶ 0007-0008, 0023-0026, 0056, 0059; Figs. 3-4; a service entity can coordinate or manage the application service via backend computing systems (e.g., a remote data center), that receive various event data from the computing devices of the users; the event data can comprise user input data corresponding to user inputs on an application interface generated on a display screen of the user's computing device; the event data can further include sensor data and/or location data from sensor and/or positioning system resources on the computing devices of the users; the event data can comprise third party data received from third party sources, such as media sources, mapping and/or traffic modeling sources, scheduling or calendar sources, and the like; computing system of the service entity can ingest the event data and generate service representations that correspond to a particular user's experience with the application service); 
obtain a user experience which is predicted based on a deterministic model including the at least one service factor (Abstract; ¶ 0008-0010, 0027-0028, 0057-0058, 0060-0061; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider); and 
wherein the neural network quantifies qualitative factors by transforming qualitative inputs into numerical inputs (¶ 0028-0032, 0052-0053; the user profile for a user can indicate how many transport requests the user has submitted, an average user rating as rated by matched transport providers, etc.; the engagement level of a user or transport provider can comprise an total amount or rate of usage of the network service, and can correspond to an actual monetary figure, a monetary rate (e.g., dollar value over time), or an abstract value (e.g., a points value) or the user or transport provider based on, for example, an average rating, feedback from matched users or transport providers, money spent using the network service, etc.).
Goldstein does not explicitly disclose but Chen does disclose wherein the deterministic model comprises a neural network having an input layer receiving inputs, multiple intermediate layers, and an output layer providing a predicted user experience value (¶ 0106-0109, 0120; Figs. 9A-9B; the variables that may determine whether the user will be lost may be determined as output variables of the predictive model; if the predictive model is a model based on the neural network algorithm (also referred to herein as a “neural network module”), the adjusting of the predictive model according to the error may include adjusting at least one of the number of the input variables of the neural network model, the number of hidden layers, the number of neurons in the hidden layers, the transfer function of the hidden layers, and the transfer function of the output layer. The adjusting of the transfer function of the hidden layers may further include adjusting the weight coefficients of the neurons).  Goldstein additionally discloses wherein the inputs include quantitative and qualitative service factors; wherein the deterministic model provides a predicted user experience value (Abstract; ¶ 0008-0010, 0024-0029, 0057-0058, 0060-0061; Figs. 3-4; a current representation for a user can indicate the status of the user, such as whether the user is on-app (e.g., currently interacting with the executing application), off-app (e.g., has deactivated the application), and the user's current state while the application is executing; the service representation can further include contextual information, such as a session time for the user interacting with the service application, a wait time for transport, changing ETAs of a matched transport provider, the transport provider's route and/or navigation information, the actual marketplace conditions, current pricing data for the network service, etc.; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider).
	Goldstein does not explicitly disclose but Shoshan does disclose receive multiple mobility as a service (MaaS) travel routes ranked based on a predicted user experience (pgs. 3, 23-24, 37-39; Figs. 14-16; certain embodiments seek to provide a MaaS system that may define and manage QoS of each ride request; the proposal pool is the set of available and valid sendee plans alternatives in which the request metric is met, and may be the full list of alternative service plans made by an exhaustive search over all possible routes; this proposal pool is further filtered, for example, by using the user/passenger model which learns the user preferences with regard to his/her rides; the proposals that are part of the proposals pool may be ranked/prioritized for filtering; the proposals pool proposals are arranged according to a given rank/metric of the proposal; the rank may be any combination/function of the expected user experience and the price of the proposal; presenting filtered proposals to relevant passengers; the arrangement of the proposals may be used also at the presentation operation).  Goldstein additionally discloses wherein a predicted user experience is the predicted future user experience (Abstract; ¶ 0008-0010, 0027-0028, 0057-0058, 0060-0061; Figs. 3-4; the service representations can be analyzed and filtered by the computing system to predict future negative user experiences; the computing system can include a prediction engine that analyzes the generated representations for each user and transport provider).
Goldstein does not explicitly disclose but Shoshan does disclose wherein the ranked MaaS travel routes are adapted based on user experience; wherein dynamic traffic information is received from the smart city management system (pgs. 3, 10, 23-24, 37-39; the QMSC may also be connected to various external data sources to get access to data that may improve the system performance; examples of such external data sources may be any road traffic congestion data source, such as road speed sensors, road cameras, etc.; certain embodiments seek to provide a MaaS system that may define and manage QoS of each ride request; the proposal pool is the set of available and valid sendee plans alternatives in which the request metric is met, and may be the full list of alternative service plans made by an exhaustive search over all possible routes; this proposal pool is further filtered, for example, by using the user/passenger model which learns the user preferences with regard to his/her rides; the proposals that are part of the proposals pool may be ranked/prioritized for filtering; the proposals pool proposals are arranged according to a given rank/metric of the proposal; the rank may be any combination/function of the expected user experience and the price of the proposal).  Goldstein additionally discloses wherein real-time information from the third party source is incorporated into the deterministic model for predicting the future user experience (Abstract; ¶ 0007-0008, 0026, 0030, 0032, 0056-0057, 0059-0060; Figs. 3-4;  using the event data, the computing system can generate representations corresponding to user experience in connection with the transport; based on the representations, the computing system can predict whether a negative user experience will occur for either the user or the transport provider at a future instance in time or over a given time interval; for example, the computing system can execute an artificial intelligence model that processes the representations to make a future prediction of whether any particular user or transport provider in the given region will have a negative user experience at a future instance; said event data may include traffic conditions, which may further be third party data received from third party sources). 
	The rationale to combine Goldstein, Chen, and Shoshan remains the same as for Claim 1.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20210112441 – “Transportation Operator Collaboration System,” Sabella et al, disclosing a method for sharing information among multiple MaaS systems for the prediction of quality of service
PGPub 20200394740 – “Identifying High Risk Trips Using Continuous Call Sequence Analysis,” Yi et al, disclosing a ridesharing system for tracking information and detecting patterns in said information for the purpose of predicting trip risks 
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK C CLARE whose telephone number is (571)272-8748. The examiner can normally be reached Monday-Friday 6:30am-2:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman can be reached at (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.





/MARK C CLARE/Examiner, Art Unit 3628                                                                                                                                                                                                        
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628                                                                                                                                                                                                        


    
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
    


Cookies help us deliver our services. By using our services, you agree to our use of cookies.