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 the Claims
Claims 1-16 and 18-21 are all the claims pending in the application.
Claim 17 is canceled.
Claim 21 is new.
Claims 1-16 and 18-20 are amended.
Claims 1-16 and 18-21 are rejected.
The following is a Final Office Action in response to amendments and remarks filed June 11, 2026.
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the claims do not recite an abstract idea. Examiner respectfully does not find this assertion persuasive because managing product development and production is an abstract idea (i.e., commercial interactions).
Second, under Step 2A Prong 2, Applicant asserts the claims integrate the abstract idea into a practical application because the limitations reduce an extent of processing resources. Examiners respectfully does not find this assertion persuasive because removing old data is a part of managing product development (e.g., removing outdated data).
Third, under Step 2B, Applicant asserts the claims reflect unconventional steps. Examiner respectfully does not find this assertion persuasive because Applicant’s remarks amount to a general allegation that the claims define an eligible invention without specifically pointing out how the language of the claims reflect unconventional steps. Accordingly, the 101 rejections are maintained, please see below for the complete rejections of the claims as amended.
Regarding the 102 rejections, the rejections are withdrawn because the cited reference does not teach the newly amended limitations. Please see below for the new 103 rejections.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
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 and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims are directed to a process 1-9 and 21 ; and claims 10-16 and 18-20 are directed to a machine.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
… generating and maintaining, by one or more processors, an architecture of data associated with a development and production of a product, wherein the architecture of data comprises previous product data determined to have at least a predetermined degree of similarity with the product;
receiving, by one or more processors, additional data for the architecture of data;
responsive to receiving the additional data for the architecture of data, removing, by one or more processors, oldest data from the architecture of data, wherein the oldest data is substantially equal in size to that of the additional data;
identifying, by one or more processors using a trained artificial intelligence (Al) model, within the architecture of data, processes and sub-processes of the processes;
identifying, by one or more processors using the trained Al model, inefficiencies within the processes and sub-processes, wherein the inefficiencies are based on predetermined research and development priorities including: time, quality and cost;
determining, by one or more processors using the trained Al model, solutions for mitigating the identified inefficiencies; and
incorporating, by one or more processors using the trained Al model, the solutions into the development and production of the product.
These limitations recite an abstract idea because these limitations encompass commercial or legal interactions because these limitations encompass steps in the product development process. That is, generating and maintaining the development and production of a product, identifying processes and sub-processes, receiving new data and erasing old data, identifying inefficiencies, determining solutions, and incorporating the solutions are all a part of managing product development and production. Claims that encompass commercial or legal interactions fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Claims 1, 10, and 19 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
… generating and maintaining, by one or more processors, an architecture of data associated with a development and production of a product, wherein the architecture of data comprises previous product data determined to have at least a predetermined degree of similarity with the product;
receiving, by one or more processors, additional data for the architecture of data;
responsive to receiving the additional data for the architecture of data, removing, by one or more processors, oldest data from the architecture of data, wherein the oldest data is substantially equal in size to that of the additional data;
identifying, by one or more processors using a trained artificial intelligence (Al) model, within the architecture of data, processes and sub-processes of the processes;
identifying, by one or more processors using the trained Al model, inefficiencies within the processes and sub-processes, wherein the inefficiencies are based on predetermined research and development priorities including: time, quality and cost;
determining, by one or more processors using the trained Al model, solutions for mitigating the identified inefficiencies; and
incorporating, by one or more processors using the trained Al model, the solutions into the development and production of the product.
These limitations do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the processor and the architecture of data, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality (i.e., as generic computer and memory) such that in amounts to no more than mere instructions to apply the exception using generic computer components.
Second, the additional elements of using the trained AI model to perform various functions, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception.
Claims 10 and 19 further recite “one or more computer-readable storage media; and program instructions stored on the one or more storage media”; and “a processor set; one or more computer-readable storage media; and program instructions stored on the one or more storage media”, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 1, 10, and 19 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent 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 abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 10 and 19 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Regarding claims 2, 11, and 20, claims 2, 11, and 20 recite the same abstract idea as the independent claims because the product being a vehicle and the various claimed processes are all a part of the product development process for a specific industry.
Regarding claims 3, 4, 12 and 13, claims 3, 4, 12 and 13 essentially recite the additional elements of using reinforcement learning and a reward function. The additional elements of applying a first using reinforcement learning, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e., machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Regarding claims 5 and 14, claims 5 and 14 recite the same abstract idea as the independent claims because identifying points along parallel logical paths and causing some paths to be performed and others not be performed is a part of the product development process (i.e., determining which actions to take during the product lifecycle and which not to).
Claims 5 and 14 further recite the additional elements of using the trained AI model to perform various functions. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception.
Regarding claims 6-8, 15 and 16 recite the same abstract idea as the independent claims because utilizing various paths that result less time, less cost, and more quality are all a part of the product development process.
Regarding claims 9 and 18, claims 9 and 18 recite the same abstract idea as the independent claims because identifying points in processes that do not satisfy thresholds and removing points in the process is a part of the product development process (i.e., determining which actions will not achieve cost or quality requirements).
Claims 9 and 18 further recite the additional elements of using the trained AI model to perform various functions. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception.
Claim 21 recites the same abstract idea as the independent claims because splitting a serial development process into parallel paths is a part of the product development process (e.g., identifying steps in the process that can be performed concurrently to reduce development time).
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, 5, 9, 10, 14, 18, 19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jawahir et al., US Pub. No. 2021/0056473, herein referred to as “Jawahir” in view of Tu et al. US Pub. No. 2006/0250248, herein referred to as “Tu”.
Regarding claim 1, Jawahir teaches:
generating and maintaining, by one or more processors, an architecture of data associated with a development and production of a product (system for managing a product life cycle, e.g., ¶¶[0008], [0044]; see also e.g., ¶¶[0010]-[0011], [0043] discussing processors),
wherein the architecture of data comprises previous product data determined to have at least a predetermined degree of similarity with the product (predicts life cycle using historical performance data of one or more similar products, e.g., ¶¶[0012], [0046], [0049]);
identifying, by one or more processors using a trained artificial intelligence (Al) model, within the architecture of data, processes and sub-processes of the processes (trains and uses machine learning algorithm, e.g., ¶¶[0012], [0044]-[0045] to determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043]-[0044] and Figs. 4 and 7);
identifying, by one or more processors using the trained Al model, inefficiencies within the processes and sub-processes (determines combinations to maximize value, ¶[0038]; see also e.g., ¶¶[0007], [0042], [0043], [0050] discussing various optimizations),
wherein the inefficiencies are based on predetermined research and development priorities including: time, quality and cost (processes maximizes productivity and quality and minimizes costs, ¶¶[0007], [0039]; see also ¶¶[0046], [0050] discussing assessing costs, etc.);
determining, by one or more processors using the trained Al model, solutions for mitigating the identified inefficiencies (determines combinations to maximize value, ¶[0038]; see also e.g., ¶¶[0007], [0042], [0043], [0050] discussing various optimizations);
and incorporating, by one or more processors using the trained Al model, the solutions into the development and production of the product (selects optimal product configuration for the product, ¶[0051]; see also ¶[0052] discussing an example of configuring products).
However, Jawahir does not teach but Tu does teach:
receiving, by one or more processors, additional data for the architecture of data responsive to receiving the additional data for the architecture of data, removing, by one or more processors, oldest data from the architecture of data, wherein the oldest data is substantially equal in size to that of the additional data (receives real-time product data and removes old data, ¶¶[0069], [0073]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management of Jawahir with the removal of old data of Tu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized outdated product data is likely not as useful as newer data and accordingly would have modified Jawahir to remove old data, e.g., as taught by Tu.
Regarding claim 5, the combination of Jawahir and Tu teaches all the limitations of claim 1 and Jawahir further teaches:
wherein: identifying, by one or more processors using the trained AI model, inefficiencies within the processes and sub-processes comprises: identifying, by one or more processor using the trained AI model, a first of the processes having a starting point and a destination point (determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043]-[0044] and Figs. 4 and 7),
wherein the first process includes a plurality of sub-processes that, along a serial logical path, include associated points between the starting point and the destination point (determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043] and Figs. 4 and 7),
determining, by one ore more processors using the trained AI model, solutions for mitigating the identified inefficiencies comprises: splitting, by the one or more processors using the trained AI model, the sub-processes along the serial logical path into a plurality of parallel logical paths each having the points of an associated one of the sub-processes (products are designed in parallel to the manufacturing and reverse manufacturing processes, ¶¶[0036], [0040]; see also ¶[0046] alternative design features),
and incorporating, by the one or more processors using the trained AI model, the solutions into the development and production of the product comprises: performing, by one or more processors using the trained AI model, the sub-process of a first of the parallel logical paths during the development and production of the product, and refraining from performing, by one or more processors using the trained AI model, the sub-processes of a remainder of the parallel logical paths during the development and production of the product (makes predictions about product life cycle and if the prediction does not satisfy predetermined environmental impact, life cycle cost, resource consumption, etc. goals and inputs one or more alternative design features and re-valuates the altered product design life cycle prediction, ¶[0046]).
Regarding claim 9, the combination of Jawahir and Tu teaches all the limitations of claim 1 and Jawahir further teaches:
wherein: identifying, by one or more processors using the trained AI model, inefficiencies within the processes and sub-processes comprises: identifying, by one or more processors using the trained AI model, a first of the processes having a starting point and a destination point (determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043]-[0044] and Figs. 4 and 7),
wherein the first process includes a sub-process having associated points between the starting point and the destination point (determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043]-[0044] and Figs. 4 and 7),
determining, by one or more processors using the trained AI model, solutions for mitigating the identified inefficiencies comprises: identifying, by one or more processors using the trained AI model, a first of the points of the sub-process that fails to satisfy thresholds associated with the predetermined research and development priorities; and incorporating, by one or more processors using the trained AI model, the solutions into the development and production of the product comprises: removing, by one or more processors using the trained AI model, the first point from the sub-process during performance of the development and production of the product (makes predictions about product life cycle and if the prediction does not satisfy predetermined environmental impact, life cycle cost, resource consumption, etc. goals and inputs one or more alternative design features and re-valuates the altered product design life cycle prediction, ¶[0046]).
Regarding claim 21, the combination of Jawahir and Tu teaches all the limitations of claim 1 and Jawahir further teaches:
wherein the solutions comprise splitting points of a first product development sub-process, of the sub-processes, along a logical path into a plurality of parallel logical paths each having points corresponding to the first product development sub-process (products are designed in parallel to the manufacturing and reverse manufacturing processes, ¶¶[0036], [0040]; see also ¶¶[0003], [0039] noting system solves problems associated with linear manufacturing processes).
Regarding claims, 10 and 19, claims 10 and 19 recites similar limitations as claim 1 and further recite: “one or more computer-readable storage media; and program instructions stored on the one or more storage media to perform operations” and “a processor set; one or more computer-readable storage media; and program instructions stored on the one or more storage media to cause the processor set to perform operations”, respectively. Jawahir teaches these concepts, e.g., ¶[0043]. Accordingly claims 10 and 19 are rejected for similar reasons as claim 1.
Claims 14 and 18 recites similar limitations as claims 5 and 9 and accordingly are rejected for similar reasons as claims 5 and 9.
Claim(s) 2, 3, 11, 12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jawahir and Tu further in view of DeCastro et al., US Pub. No. 2023/0131741, herein referred to as “DeCastro”.
Regarding claim 2, the combination of Jawahir and Tu teaches all the limitations of claim 1 and Jawahir further teaches:
and the processes are selected from the group consisting of: determining a start-up plan for the vehicle, defining production of the vehicle, preparing for initiation of the determined start-up plan, initiating the determined start-up plan, anticipating delays, determining conditions of the vehicle, research and development of the vehicle, identifying a pilot for the vehicle, producing a sample lot of the vehicle, and performing quality control based on the sample lot of the vehicle (system covers initial design and manufacturing of the product, ¶¶[0036], [0044] and Fig. 7).
However, the combination of Jawahir and Tu does not teach but DeCastro does teach:
wherein the product is a vehicle (designs cars, e.g., ¶¶[0014], [0016])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management of Jawahir and Tu with the car design of DeCastro because use of known technique to improve similar methods in the same way is obvious, see MPEP 2143.I(C). That is, Jawahir teaches a process for improving a product life cycle management, one of ordinary skill would have recognized this process could be applied to the design of many products, including design of cars as taught DeCastro.
Regarding claim 3, the combination of Jawahir and Tu teaches all the limitations of claim 1 and Jawahir further teaches:
training, by one or processors, an AI model to identify training processes and training sub-processes using a predetermined training set of data (trains and uses machine learning algorithm, e.g., ¶¶[0012], [0044]-[0045] to determine stages in the manufacturing process and movement between stage-node combinations and transfer points, ¶¶[0038], [0043] and Fig. 4),
wherein training the AI model (i) generates the trained AI model (uses training set to train model, ¶¶[0047]-[0048] and Fig. 9)
and (ii) comprises analyzing, by one or more processors using the AI model, the predetermined training set of data (uses training set to train model, ¶¶[0047]-[0048] and Fig. 9)
However, the combination of Jawahir and Tu does not teach but DeCastro does teach:
predicting, by one or more processors using the AI model, as to the training processes and the training sub-processes within the predetermined training set of data (reward-based models predicts output, ¶[0016]; see also ¶[0034] discussing reinforcement learning);
responsive to a determination that the prediction is correct, providing, by one or processors, reward based feedback to the AI model to adjust accuracy of the model (uses a reward based model to predict an output based on the likelihood of receiving positive reward, ¶[0016]; see also ¶[0034] discussing reinforcement learning).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management of Jawahir with the reinforcement learning of DeCastro because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have modified the machine learning of Jawahir to use reinforcement learning in situations where reinforcement learning is advantageous (e.g., when adaptability of the model is desired).
Regarding claims 11, 12, and 20, claims 11, 12, and 20 recite similar limitations as claims 2 and 3 and accordingly are rejected for similar reasons.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jawahir and Tu, further in view of DeCastro, further in view of Abbott, US Pub. No. 2021/0240145, herein referred to as “Abbott”.
Regarding claim 4, the combination of Jawahir, Tu and DeCastro teaches all the limitations of claim 3 and does not teach but Abbott does teach:
responsive to determining that the accuracy of the Al model exceeds a predetermined threshold of accuracy, determining, by one or more processors, that the Al model is trained and is the trained Al model. (training is completed based on a minimum accuracy being reached, ¶[0057]; see also ¶[0151] discussing product development).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management with the reinforcement learning of Jawahir, Tu and DeCastro with the completion of training based on a threshold accuracy of Abbott because Jawahir suggests doing so, see MPEP 2143.I(G). That is, Jawahir teaches training the model, e.g., ¶[0047]. One of ordinary skill would have recognized the training would need to be completed based on benchmarks, e.g., the accuracy threshold taught by Abbott.
Regarding claim 13, claim 13 recites similar limitations as claim 4 and accordingly is rejected for similar reasons.
Claim(s) 6-8, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jawahir and Tu further in view of King et al., US Pub. No. 2022/0214668, herein referred to as “King”.
Regarding claim 6, the combination of Jawahir and Tu teaches all the limitations of claim 5 and does not teach but King does teach:
wherein the performance of the first of the parallel logical paths results in the development and production of the product in less time than a resulting time of a performance of any of the logical paths of the remainder of the parallel logical paths for the development and production of the product (identifies one or more product feature variations, manufacturing detail variations, and/or product quantity variations that optimize for manufacturing speed, ¶[0109]; see also ¶[0170] discussing analyzing alternatives to optimize delivery times).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management of Jawahir and Tu with development platform of King because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have modified the analysis Jawahir to optimize for specific variables like time, e.g., as taught by King.
Regarding claim 7, the combination of Jawahir, Tu and King teaches all the limitations of claim 6 and King further teaches:
wherein the performance of the first of the parallel logical paths results in the development and production of the product for less cost than a cost associated with a performance of any of the logical paths of the remainder of the parallel logical paths for the development and production of the product (identifies one or more product feature variations, manufacturing detail variations, and/or product quantity variations that optimize for cost, ¶[0109]; see also ¶[0170] discussing analyzing alternatives to optimize cost considerations).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the product life cycle management of Jawahir and Tu with development platform of King because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have modified the analysis Jawahir to optimize for specific variables like cost, e.g., as taught by King.
Regarding claim 8, the combination of Jawahir, Tu and King teaches all the limitations of claim 7 and Jawahir further teaches:
wherein the performance of the first of the parallel logical paths results in the development and production of the product with more product quality than a product quality associated with a performance of any of the logical paths of the remainder of the parallel logical paths for the development and production of the product (optimizes product/process quality, ¶¶[0039], [0053]).
Regarding claims 15 and 16, claims 15 and 16 recite similar limitations as claims 6 and 7 and accordingly are rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Schöning et al., DE102021211356 teaches a similar method for analyzing vehicle design and production
Sethuraman et al., US Pub. No. 2023/0206287 teaches a similar method for analyzing product development lifecycle
Meharwade et al., US Pub. No. 2019/0050771 teaches a similar process for machine learning based product development
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 BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 11 - 7.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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