DETAILED ACTION
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 Non-Final Office Action is in reply to the communications filed on 15 January 2026.
Claims 4, 12 have been canceled.
Claims 1-3, 5-11 and 13-20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 15 January 2026 has been entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a stakeholder persona training module to in claims 17, 18 and 20
a stakeholder simulation engine to in claim 17
a stakeholder score aggregation engine to in claims 17, 19
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Previous Claim Rejections - 35 USC § 112
Examiner withdraws the 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph of claims 1-3, 5-8 in view of the amendments.
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 1-3, 5-11 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1, the claims are analyzed to determine whether the claims fall within any statutory category. Claims 1-3, 5-8 recite a method (i.e. a process), claims 9-11, 13-16 recite a non-transitory computer-readable medium (i.e. manufacture) and claims 17-20 recite a system (i.e. a machine). Thus the claims fall within at least one of the four statutory categories. See MPEP 2106.03.
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Claims 1, 9 and 17 recite the abstract idea, simulating, using the plurality of stakeholder models, the plurality of stakeholder personas in the vehicle design review process of a potential vehicle design; aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential vehicle design; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential vehicle design, modifying the potential vehicle design based on the summary and associated bias scores and generating a vehicle design based on a modification of the potential vehicle design according to the summary.
The claims can be considered to fall within the mental process grouping because the limitations cover concepts performed in the human mind by observation, evaluation, judgment, and opinion. The “simulating” step encompass a human mentally mimicking the personas using models. The “aggregating” step in the context of the claims encompass a human observing and gathering scores. The ”modifying” step encompass a human mentally adjusting the potential vehicle design based on the observed summary and bias scores and the “generating” step encompass a human evaluating modifications to a potential vehicle design and creating a vehicle design either mentally or with pen and paper. See MPEP 2106.04(a)(2), subsection III.
Claims 3 and 11 further narrow the abstract idea as identified in claims 1 and 9 by describing the types of scores.
Claims 5, 13 and 18 recite the abstract idea, modifying, over time, the plurality of stakeholder models corresponding to the plurality of stakeholder personas. Claims 6, 14 and 19 recite the abstract idea, calculating a clarity score, an alignment score, and/or an enthusiasm score for each stakeholder persona model. Claims 8 and 16 recite the abstract idea, receiving the associated bias scores.
Claims 5-6, 8, 13-14, 16, 18 and 19 can be considered to fall within the mental process grouping because the limitations cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. The “calculating” step encompass a human mentally evaluating a score.
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
Claims 1-3, 5-11 and 13-20 do not recite additional elements that integrate the judicial exception into a practical application.
Claim 9 recites the additional elements, non-transitory computer-readable medium having program code recorded thereon for machine-assisted collaborative vehicle design, the program code being executed by a processor to perform the claimed steps. However, the additional elements are computing components recited at a high level of generality to perform the generic functions of training, simulating, aggregating and displaying information such that they amount to no more than mere instructions to apply the exception using generic computing components.
Claim 17 recites the additional elements, a stakeholder persona training module, a stakeholder simulation engine, stakeholder score aggregation engine, and a feedback report display module. However, the additional elements are computing components recited at a high level of generality to perform the generic functions of training, simulating, aggregating and displaying information such that they amount to no more than mere instructions to apply the exception using generic computing components.
Claims 1, 9 and 17 recite the additional element, displaying a summary providing an overview of the aggregated individual scores regarding the potential vehicle design to a user. However, these limitations amount to mere data output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Claims 1, 9, 17 recite the additional element, training a generative neural network to simulate a plurality of stakeholder personas in a product review process using a discriminator neural network to feed realism scores based on responses of the generative neural network to provide a plurality of stakeholder models. Claims 7, 15 and 20 recite the additional element, training the neural network comprises: training the neural network to simulate a stakeholder persona in the product review process to provide a stakeholder persona model; and repeating the training of the neural network for a plurality of different stakeholder personas to provide the plurality of stakeholder model. Claim 8 recites the additional element, training the neural network relative to the associated bias scores. The additional elements of “training the neural network” add the words apply it (or an equivalent) or mere instructions to implement the abstract idea on a computer, as discussed in MPEP 2106.05(f). The steps of training are simply used to provide and update stakeholder models.
Similar to claims 1, 9 and 17, the additional elements of dependent claims 3-8, 11-16 and 17-20, include computing components recited at a high level of generality to perform the generic functions of the abstract idea such that it amounts to no more than mere instructions to apply the exception using generic computing components.
Claims 2 and 10 recite the additional elements, providing a written description of the potential vehicle design as an input to the plurality of stakeholder models. However, these limitations are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under Step 2B the claims are analyzed to determine whether the claims recite additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
As a whole, claims 1-3, 5-11 and 13-20 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 abstract idea into a practical application, the additional elements amount mere instructions to apply the exception using generic computer components. Instead, the computing components are being used as tools to perform the abstract idea such that they provide nothing more than generally linking the use of the abstract to a particular technological environment or field of use. See MPEP 2106.05(f & h). For the same reasons, the recited elements are insufficient to provide an inventive concept and fail to impose any meaningful limits on practicing the abstract idea.
For the displaying and providing steps that were considered extra-solution activity in Step 2A, Prong Two, this has been re-evaluated in Step 2B and determined to be well understood, routine, and conventional in the field. The Ultramercial, Symantec, TLI, and OIP Techs. court decisions indicate that mere transmission, and presenting of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. See MPEP 2106.05(d), subsection II.
Considered as an ordered combination, the additional elements of the claim do not add anything further than when they are considered separately. Thus, under Step 2B, the claims are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
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.
Claim(s) 1-3, 5-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yannakakis et al (US 2020/0298118 A1) in view of Movert et al (US 2019/0176818 A1) in view of White (US 2021/0326494 A1).
Claims 1, 9 and 17: Yannakakis discloses a method for collaborative product design, (see [0028]: These AI personas can be used, for example, to progress through a game much faster than an actual player to evaluate game content more quickly; to assess the difficulty of levels with randomness with thousand variations of playthroughs; to generate key performance indicators (KPIs), to increase the speed of design iteration, to free up designers' time to focus on gameplay and high level concepts), a non-transitory computer-readable medium having program code recorded thereon for machine-assisted collaborative product design, the program code being executed by a processor (see [0252]: The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. [0253]); and a system for machine-assisted collaborative product design (see Fig. 2):
training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models (see [0031]: a convolutional neural network, stacking neural networks, a generative adversarial network, or other deep learning algorithm that is iteratively trained based on game telemetry data, behavioral motivation data and/or game play by one or more AI personas. [0036] The BEA tools 254 can be constructed via preference learning or other machine learning techniques that are trained, for example, based on player questionnaires, game telemetry data or other game data in order to learn and predict actual player motivations. Once trained, the BEA tools 254 use game telemetry data from other players/viewers to predict individual players'/viewers' reasons for interacting with a game);
simulating, using the plurality of stakeholder models, the plurality of stakeholder personas in the product review process of a potential product (see [0119] In addition, consider the following further example for obtaining computational models of player experience that are generative and general (e.g. “general experience personas”). The personas are generative as they are able to simulate the experience of players which is provided as human experience demonstrations. This process is also general across the various instantiations of a particular domain that involves the digitization and simulation of human experience. [0120]: the input of the model, the computation, and the output of the model. This approach can build on anchoring methods of psychology according to which humans encode values in a comparative (relative) fashion. Based on an innovative ordinal modeling approach, personas perceive humans (or their demonstrations) via generalizable features and they gradually machine learn to experience the environment as humans would do).
Yannakakis discloses training a neural network but does not expressly disclose training a generative neural network using a discriminator neural network to feed realism scores based on responses of the generative neural network. However, Movert teaches, training a generative neural network using a discriminator neural network to feed realism scores based on responses of the generative neural network (See [0039]: Further example types of a neural network may be generative and adversarial network. Generally, a generative adversarial network comprises a discriminator and a generator, both may be provided in the form of a neural network. The discriminator has to undergo training, i.e. unsupervised training based on training data. It is the discriminator that will perform the mapping of driving behavior data with a driver model once it has been trained. The discriminator may be operated by a vehicle control unit. The generator is configured to provide noise influenced data samples (i.e. “fake samples”) from a latent space to the discriminator. The discriminator is trained to distinguish between the real samples (i.e. present driving behavior data) and the fake samples. During training it is checked whether the discriminator was correct in its determination, and the training is fine-tuned based on the outcome of the discriminators decision).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the training of the neural network of Yannakakis, training a generative neural network using a discriminator neural network to feed realism scores based on responses of the generative neural network as taught by Movert in order to fine-tune the training of the neural network (Movert, [0039]).
While Yannakakis discloses designing a gaming application, Yannakakis also discloses [0128]: While described above in the conjunction with generating BEA data for games, the techniques described above can apply in other industries as well. Being able to both model and generate the experience of people can be used in any research domain or industrials sector involving human behavior and experience. The list of potential applications of the process is vast and includes sectors such as creative industries, marketing, retailing, web services, architecture and built environment, cyber physical systems, automobile industry, and the digital arts. Generative and general experience personas not only leverage the ability to test, develop and offer services faster and more efficiently. They also enable better (persona-driven) decisions all the way from ideation to prototyping, production, and release of a service, a project or an object that humans would interact with.
The sole difference between the Yannakakis as modified by Movert and the claimed subject matter is that Yannakakis does not disclose a vehicle design but White in the same field of endeavor teaches, a potential vehicle design (see [0087]: Design space is used to visually represent all possible designs for a particular product or service. Products can include automobiles).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the vehicle design of White for the gaming application design of Yannakakis as modified by Movert.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
White further discloses, aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential vehicle design; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential vehicle design (see [0175]: The behavioral model developed using task-based feedback can be used to map a given reconstructed design representation to the user characterization or classification. In some implementations, the user classification can be a scoring of good (preferred) versus bad (non-preferred). The user/human-based characterization can be used together with a cost function to create a mathematical representation or score that can be used by an optimizer to create or improve the design.); displaying a summary providing an overview of the aggregated individual scores regarding the potential product to a user (See [0175]: The user/human-based characterization can be used together with a cost function to create a mathematical representation or score that can be used by an optimizer to create or improve the design. The output of the optimizer is a design type associated with a latent factor description. The optimization could even use genetic mutation or cross-over to create a new latent factor. The new latent factor can be passed through the decoder/generator to produce a new design representation that can be inserted into a new task. The design, latent factor, and keywords all form a space of designs and distance metrics that can be used to relate or cluster one design to another in the creation of new tasks shown to users.); modifying the potential vehicle design based on the summary and associated bias scores; generating a product based on a modification of the potential vehicle design according to the summary (see Fig. 6A, [0152]: producing a modified design shape (622). For example, model generator 126 can produce a modified design shape using the behavioral models and a design space for the product. [0175]: . In some implementations, the user classification can be a scoring of good (preferred) versus bad (non-preferred) (hence bias). The user/human-based characterization can be used together with a cost function to create a mathematical representation or score that can be used by an optimizer to create or improve the design).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product development feedback system and method of Yannakakis as modified by Movert/White, aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential vehicle design; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential vehicle design, displaying a summary providing an overview of the aggregated individual scores regarding the potential product to a user; modifying the potential vehicle design based on the summary and associated bias scores; and generating a product based on a modification of the potential vehicle design according to the summary as taught by White “ for rapid design development of products that more accurately and dependably meet consumer demand and expectations” (White, [0022]).
Claims 2 and 10: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1, and 9 above. White further teaches, providing a written description of the potential vehicle design as an input to the plurality of stakeholder models (see [0076]: he response data can include semantic descriptors, provided by the user, regarding a task, product, or design. Semantic descriptors can include any descriptors that provide semantic information about an object, such as a product or design. [0127] Flow 500 continues with building one or more behavioral models based on the user response data (514). An example of a behavioral model includes the use of linear or nonlinear function approximation (e.g. deep learning enabled convolutional neural networks) to map designs, design features, design deformations or graph based design representation to a semantic description or rating.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product development feedback system and method of Yannakakis as modified by Movert and White, providing a written description of the potential vehicle design as an input to the plurality of stakeholder models as taught by White so that “models can be used to explore or optimize the model space to identify new designs or semantic descriptions that get included in future tasks” (White, [0129]).
Claims 3 and 11: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1 and 9 above. Yannakakis teaches an enthusiasm score (see [0145]: motivation score). Yannakakis also teaches different factors of the scores (see [0170]). Yannakakis and Sharma do not expressly disclose the attributes of the individual scores comprise a clarity score, and an alignment score. However, the Examiner asserts that the data identifying the scores as including clarity score and alignment score is simply a label for the score and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type of score) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to have the clarity score and alignment score be included in the score information of Yannakakis as modified by Movert and White because the type of score attribute does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention.
Claims 5, 13 and 18: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1, 9, and 17 above. Yannakakis further teaches, modifying, over time, the plurality of stakeholder models corresponding to the plurality of stakeholder personas (see [0133]: Updating the gaming bot 250 can also include iteratively adjusting the gaming bot to adjusted gaming bot configurations via a search algorithm on the parameters of the gaming bot, iteratively generating updated difference data corresponding to the adjusted gaming bot configurations, and accepting one of the adjusted gaming bot configurations when the correspond).
Claims 6, 14 and 19: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1, 9, and 17 above. Yannakakis further teaches, calculating a clarity score, an alignment score, and/or an enthusiasm score for each stakeholder persona model (see [0150]: combining motivational score (hence enthusiasm score)).
Claims 7, 15 and 20: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1, 9, and 17 above. Yannakakis further teaches, training the neural network to simulate a stakeholder persona in the product review process to provide a stakeholder persona model; and repeating the training of the neural network for a plurality of different stakeholder personas to provide the plurality of stakeholder models (see [0031]: PCG tools 252 are constructed via constructive algorithm, generate-and-test algorithm, search-based algorithm, and/or machine learning algorithm and include, for example, a convolutional neural network, stacking neural networks, a generative adversarial network, or other deep learning algorithm that is iteratively trained based on game telemetry data, behavioral motivation data and/or game play by one or more AI personas) .
Claims 8 and 16: The combination of Yannakakis, Movert and White discloses the claimed invention as applied to claims 1 and 9 above. Yannakakis further teaches, in which training the neural network comprises: receiving the associated bias scores; and training the neural network relative to the associated bias scores (see [0157]: a supervised machine learning technique, in which an algorithm learns to infer the preference relation between two variables. The BEA tools 254 adopt preference learning (PL) models because of the strong connection between this ordinal machine learning paradigm and how player experience operates in games. In essence, PL models certain psychological processes by focusing on the differences between occurrences instead of their absolute values. This approach has the advantage that it aligns more closely to the players' cognitive processes—e.g. anchoring-bias).
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive. Applicant argues, “that the absence of explicit "means for" language rebuts the presumption that the claim element of claims 17-20 are to be treated in accordance with 35 U.S.C. § 112(f).” However, the examiner respectfully disagrees. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier (See MPEP 2181 subsection I).
With respect to the 35 U.S.C. §101 rejection, examiner asserts that the amendments are insufficient to overcome the rejection. Applicant argues that "training a generative neural network...solves the technical problem of model accuracy training". However as recited in the claims and illustrated in the specification [0065-0066] the trained generative neural network is used as a tool to generate stakeholder models. The claims are not rooted in machine learning technology and the claims do not solve a technical problem that only arises in machine learning technology. Furthermore, Applicant has failed to explicitly explain how and why the amended claim limitations provides a tangible vehicle design that is significantly more, beyond an abstract idea. Examiner maintains that the claims are directed to an abstract idea and the claims do not recite additional elements that amount to an inventive concept.
Applicant’s arguments with respect to the claim rejections under 35 U.S.C. §103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAAME BALLOU whose telephone number is (571)270-1359. The examiner can normally be reached Monday-Friday 9am-5pm.
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MAAME BALLOU
Examiner
Art Unit 3629
/MAAME BALLOU/Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626