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 .
Claims 1-20 have been presented for examination based on the application filed on 12/23/2022.
Claim(s) 1-3, 5-9, 11-15, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20140365333 A1 by Hurewitz; Matthew, in view of US 20210158406 A1 by Fox; Jeremy R. et al..
Claim(s) 4, 10, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20140365333 A1 by Hurewitz; Matthew, in view of US 20210158406 A1 by Fox; Jeremy R. et al., further in view of US 20210279377 A1 by Kuniavsky; Michael et al.
This action is made Non-Final.
Examiner Note
Applicant filed request for deferral of examination under 37 C.F.R. § 1.103(d), filed 10/31/2023. The request was APPROVED. The examination of the application was deferred for a period of 36 (thirty-six) months from 12/23/2022, ending in 12/23/2025. The action is herein is presented pursuant to termination of the suspension period.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5-9, 11-15, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20140365333 A1 by Hurewitz; Matthew, in view of US 20210158406 A1 by Fox; Jeremy R. et al..
Regarding Claim 1
Hurewitz teaches A method (Hurewitz: Fig.12 [0076]-[0088] & Fig.15) , comprising: converting, by a processor set, a digital twin model of a physical product having a primary design to a virtual digital twin model enabling user interactions with features of the virtual digital twin model in a virtual environment (Hurewitz: Fig.12 steps 1210-1230 & [0076]-[0088] teaching digital twin as virtual interactive product that is created from 3D rendered images for virtual display in interactive environment);
collecting, by the processor set, user interaction data generated from virtual interactions of users with the features of the virtual digital twin model in the virtual environment (Hurewitz: Fig.12 steps 1240-1260 & [0076]-[0088] as gesture data);
generating, by the processor set, sentiment data indicating a sentiment of the users associated with the virtual interactions of the users with the features of the virtual digital twin model (Hurewitz: Fig.12 step 1270 & [0085]-[0088] sentiment data as emotional response) ; and
inputting, by the processor set, the user interaction data (Hurewitz: Fig.12 12 steps 1240-1260 & [0076]-[0088] as gesture data) , the sentiment data (Hurewitz: Fig.12 step 1270 & [0085]-[0088] sentiment data as emotional response), and different groups of the users (Hurewitz: Fig.12 step 1270 & [0085]-[0088] "...[0088] In one embodiment the emotional response information could be combined with customer-identifying information....") into a trained machine learning (ML) predictive model (Hurewitz: [0077]-[0078]) , (Hurewitz: [0087] "... gesture analytic data is preferably aggregated from many different customers 135. The manufacturer can use the emotional response information to determine which product features are liked and disliked by consumers, and therefore improve product design to make future products more user-friendly....") .
Hurewitz teaches using machine learning for user classification and teaches its results can be provided to product designer for designing the product (Hurewitz: Fig.12 step 1280 & [0088]; ML is [0076]-[0078]).
Hurewitz does not specifically teach inputting, by the processor set, the user interaction data, the sentiment data, and different groups of the users into a trained machine learning (ML) predictive model, thereby generating, as an output of the ML predictive model, a different secondary design of the physical product for each of the different groups of users.
Fox teaches inputting, by the processor set, the user interaction data, the sentiment data, and different groups of the users into a trained machine learning (ML) predictive model, thereby generating, as an output of the ML predictive model, a different secondary design of the physical product (Fox: Fig.2 step 210/214 flows, e.g. in [0026] "... Specifically, the cognitive product design program 108A, 108B may use the data mining and machine learning techniques as well natural language processing techniques to parse, analyze, and compare user feedback and user reviews. Thereafter, based on the user feedback and reviews, the cognitive product design program 108A, 108B may determine an overall or most popular feedback or sentiment that may be associated with a majority of users and may regard, for example, a particular product/service and/or a particular feature of a product/service. ..." Fig.2 step 220 actual manufacture /producing /design of product done by designed product [0039] "... [0039] Thereafter, at 220, the cognitive product design program 108A, 108B may produce the product or service based on the specification...." ) for each of the different groups of users (Fox: [0024] product for different groups; e.g. [0030], [0037]-[0038] different group between 20 and 30 years old, 30 years old or more, 13 years old or less – different design for each group) .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Fox to Hurewitz for "... generating the machine learning-based product and service specification based on the received input, the one or more categories of users, the first set of online feedback, and the second set of online feedback...." thereby complementing Hurewitz in product design(Fox : Abstract). The motivation to combine would have been that while Hurewitz and Fox both use machine learning to process user feedback/sentiment, Fox uses machine learning to further "... the cognitive product design program 108A, 108B may use natural language processing techniques to determine whether a user's product/service feedback includes one or more suggestions on how to improve a product/service and/or a particular feature of a product/service...." (Fox: [0022]-[0023]). Further motivation to combine would be that Hurewitz & Fox are analogous art to the instant claim in the field of using user feedback/sentiment as input to leverage machine learning based product design (Fox: Abstract; Hurewitz: Abstract).
Regarding Claim 2
Hurewitz & Fox teaches the method of claim 1, further comprising classifying, by the processor set, the user interaction data to generate classified data regarding the virtual interactions of the users, wherein the inputting the user interaction data comprises inputting the classified data (Fox: [0020] classification; classified data as grouping information like age or profession in [0024]; Hurewitz: [0088] "... [0088] In one embodiment the emotional response information could be combined with customer-identifying information....") .
Regarding Claim 3
Fox teaches the method of claim 1, wherein the ML predictive model generates the different secondary designs of the physical product based on a number of users in each of the different groups of users meeting a threshold number of users (Fox: [0030], [0037]-[0038] – threshold number could be age group between 20 and 30 years old, 30 years old or more, 13 years old or less – different design for each group).
Regarding Claims 5 & 13
Hurewitz teaches the method of claim 1, wherein the user interaction data is selected from one or more of the group consisting of: text-based data from the users1 (Hurewitz: [0043]) , audio data from the users (Hurewitz: [0033]-[0034]) , and biofeedback data from the users (Hurewitz: [0033]-[0034]) .
Regarding Claim 6
Hurewitz & Fox teaches the method of claim 1, wherein the different groups of users are classified groups of users, and the method further comprises classifying, by the processor set, the users into the classified groups of users based on the classified data and the sentiment data (Fox: [0020] classification; classified data as grouping information like age or profession in [0024], [0026][0037] sentiment data; Hurewitz: [0088] "... [0088] In one embodiment the emotional response information could be combined with customer-identifying information...."); emotion data as sentiment data to design a product in Fig.3 & [0076]).
Regarding Claims 7, 14 & 18
Hurewitz teaches the method of claim 1, further comprising creating, by the processor set, the digital twin model of the physical product based on obtained sensor data of the physical product (Hurewitz: Abstract [0033]-[0035]).
Regarding Claims 8, 15 & 19
Hurewitz & Fox teaches the method of claim 1, further comprising iteratively generating, by the processor set, additional secondary designs of the physical product for the respective groups of users (Fox : [0019]"... Thereafter, the cognitive product design program 108A, 108B may generate specification requirements for the product and/or service based on the received input, the identified categories of users, and the identified user-wide feedback, whereby the specification requirements may include one or more designs of the product or service...."; [0028], [0031], [0038] as new specification for products based on groups) as an output of the ML predictive model (Fox: Fig.2 step 210/214 flows, e.g. in [0026] "... Specifically, the cognitive product design program 108A, 108B may use the data mining and machine learning techniques…”) at different points in time by inputting additional user interaction data (Hurewitz : [0034] "... A gesture may be defined as one or more raw data points being tracked between one or more locations in one-, two-, or three-dimensional space (e.g., in the (x, y, z) axes) over a period of time...."), additional sentiment data, and additional groups of users into the ML predictive model based on additionally user interaction data collected over time (Fox: [0026] in put sentiment data "... Thereafter, based on the user feedback and reviews, the cognitive product design program 108A, 108B may determine an overall or most popular feedback or sentiment that may be associated with a majority of users and may regard, for example, a particular product/service and/or a particular feature of a product/service. ..."; [0028] "... For example, the cognitive product design program 108A, 108B may receive user input via a text box on the user interface whereby the user input includes a problem statement, which may include text and/or a natural language statement, and whereby the user wants to design a smart clock widget that includes an alarm feature to accommodate the alarm needs of various potential users in a household (i.e. children, student, parent) during various times and events of a day...."; [0035] ) showing multiple user interaction data from family at different times; [0037]) .
Regarding Claim 9
Hurewitz & Fox teaches the method of claim 8 (Hurewitz & Fox: as mapped for the parent claims 1 & 8, where the process is repeated for new inputs; See mapping with Fox for additional inputs below) , wherein the iteratively generating the additional secondary designs of the product comprises: generating, by the processor set, a new digital twin model for each of the one or more secondary designs; converting, by the processor set, the new digital twin model for each of the one or more secondary designs to a new virtual digital twin model for each of the one or more secondary designs enabling additional user interactions with a set of features of the new virtual digital twin model for each of the one or more secondary designs in the virtual environment; collecting, by the processor set, additional user interaction data generated from additional virtual interactions with the new virtual digital twin model for each of the one or more secondary designs of the product in the virtual environment; generating, by the processor set, the additional sentiment data indicating other sentiment of the users associated with the additional virtual interactions of the users; and inputting, by the processor set, the additional user interaction data, the additional sentiment data, and determined groups of users into the trained ML predictive model, thereby generating the additional secondary designs of the product for the respective ones of the determined groups of users (Fox: [0038] "... Similarly, subsequent to generating a specification for a product/service at 214, the cognitive product design program 108A, 108B may receive additional input to, for example, refine the generated specification based on additional input. More specifically, for example, the cognitive product design program 108A, 108B may receive additional input that may include a new problem statement associated with different users and/or one or more additional parameters that may restrict the specification for a particular group. Thus, according to one embodiment, the cognitive product design program 108A, 108B may use the additional input as well as the generated specification to generate a new specification at 214.
..."; Further its is clear that product design is an iterative process (Fox:[0014]) and performing the steps again with same algorithm to derive new product based on additional inputs is simply duplication of process/parts as in In re Harza).
Regarding Claim 11
Fox teaches the method of claim 8, further comprising generating and sending, by the processor set, a final list of secondary product designs to a user (Fox: [0019] "... Thereafter, the cognitive product design program 108A, 108B may generate specification requirements for the product and/or service based on the received input, the identified categories of users, and the identified user-wide feedback, whereby the specification requirements may include one or more designs of the product or service...."; Fig.3 element 210, 214, 216, 218, 220) .
Regarding Claims 12 & 17
Hurewitz teaches (Claim 12) A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (Hurewitz: [0051][0078]) , the program instructions executable to:
(Claim 17) A system (Hurewitz : Fig.3-4 & [0041] )comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions (Hurewitz : Fig.3-4 & [0041] showing processor 31, memory 350 and instructions 359) executable to:
convert a digital twin model of a physical product having a primary design to a virtual digital twin model enabling user interactions with features of the virtual digital twin model in a virtual environment, wherein the digital twin model accurately mimics real-world features of the physical product (Hurewitz: Fig.12 steps 1210-1230 & [0076]-[0088] teaching digital twin as virtual interactive product that is created from 3D rendered images for virtual display in interactive environment; real world mimicking as seen in Fig.8 [0056]-[0060] & Fig.11);
collect user interaction data generated from virtual interactions of users with the features of the virtual digital twin model in the virtual environment during gamification (Hurewitz: Fig.12 steps 1240-1260 & [0076]-[0088] as gesture data; real world gamification as virtual interaction as seen in Fig.8 [0056]-[0060] & Fig.11);
generate sentiment data indicating a sentiment of the users associated with the virtual interactions of the users with the features of the virtual digital twin model (Hurewitz: Fig.12 step 1270 & [0085]-[0088] sentiment data as emotional response); and
input the user interaction data (Hurewitz: Fig.12 12 steps 1240-1260 & [0076]-[0088] as gesture data) , the sentiment data (Hurewitz: Fig.12 step 1270 & [0085]-[0088] sentiment data as emotional response), and different groups of the users (Hurewitz: Fig.12 step 1270 & [0085]-[0088] "...[0088] In one embodiment the emotional response information could be combined with customer-identifying information....") into a trained machine learning (ML) predictive model (Hurewitz: [0077]-[0078]), (Hurewitz: [0087] "... gesture analytic data is preferably aggregated from many different customers 135. The manufacturer can use the emotional response information to determine which product features are liked and disliked by consumers, and therefore improve product design to make future products more user-friendly...."), wherein the different secondary designs each include a unique combination of the features of the physical product (Hurewitz : [0087] "...[0087] In step 1280, the analyzed emotional response data is provided to a product designer. For example, the data may be sent to a manufacturer 290 of the product. Anonymous gesture analytic data is preferably aggregated from many different customers 135. The manufacturer can use the emotional response information to determine which product features are liked and disliked by consumers, and therefore improve product design to make future products more user-friendly...."; [0100][0105]).
Hurewitz teaches using machine learning for user classification and teaches its results can be provided to product designer for designing the product (Hurewitz: Fig.12 step 1280 & [0088]; ML is [0076]-[0078]).
Hurewitz does not specifically teach inputting, by the processor set, the user interaction data, the sentiment data, and different groups of the users into a trained machine learning (ML) predictive model, thereby generating, as an output of the ML predictive model, a different secondary design of the physical product for each of the different groups of users.
Fox teaches inputting, by the processor set, the user interaction data, the sentiment data, and different groups of the users into a trained machine learning (ML) predictive model, thereby generating, as an output of the ML predictive model, a different secondary design of the physical product (Fox: Fig.2 step 210/214 flows, e.g. in [0026] "... Specifically, the cognitive product design program 108A, 108B may use the data mining and machine learning techniques as well natural language processing techniques to parse, analyze, and compare user feedback and user reviews. Thereafter, based on the user feedback and reviews, the cognitive product design program 108A, 108B may determine an overall or most popular feedback or sentiment that may be associated with a majority of users and may regard, for example, a particular product/service and/or a particular feature of a product/service. ..." Fig.2 step 220 actual manufacture /producing /design of product done by designed product [0039] "... [0039] Thereafter, at 220, the cognitive product design program 108A, 108B may produce the product or service based on the specification...." ) for each of the different groups of users (Fox: [0024] product for different groups) .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Fox to Hurewitz for "... generating the machine learning-based product and service specification based on the received input, the one or more categories of users, the first set of online feedback, and the second set of online feedback...." thereby complementing Hurewitz in product design(Fox : Abstract). The motivation to combine would have been that while Hurewitz and Fox both use machine learning to process user feedback/sentiment, Fox uses machine learning to further "... the cognitive product design program 108A, 108B may use natural language processing techniques to determine whether a user's product/service feedback includes one or more suggestions on how to improve a product/service and/or a particular feature of a product/service...." (Fox: [0022]-[0023]). Further motivation to combine would be that Hurewitz & Fox are analogous art to the instant claim in the field of using user feedback/sentiment as input to leverage machine learning based product design (Fox: Abstract; Hurewitz: Abstract).
Claim(s) 4, 10, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20140365333 A1 by Hurewitz; Matthew, in view of US 20210158406 A1 by Fox; Jeremy R. et al., further in view of US 20210279377 A1 by Kuniavsky; Michael et al.Regarding Claim 4
Teachings of Hurewitz & Fox are shown in the parent claim 1. Hurewitz & Fox do not explicitly teach limitations of this claim 4.
Kuniavsky teaches the method of claim 1, wherein the ML predictive model generates the different secondary designs of the physical product based on stored cost versus benefits rules and manufacturing information regarding features of the physical product (Kuniavsky: Abstract "... A set of candidate product designs for the product are generated based on the product template, each style grammar, and the one or more physical constraints. A set of scores are generated for each candidate product design based on an evaluation of the candidate product designs. A subset of the candidate product designs are selected based on the scores...." [0066] "... The user's visual preferences can also be determined, for example, by collecting data on the most popular product designs sold, selecting product designs that look most like the user's past designs, and/or using an individual's selections of past product designs that the user preferred. The user's visual preferences can be determined using conjoint analysis, sentiment analysis, and/or genetic algorithms....".: [0071] "... [0071] The scores can also include a design cost. The design cost for a candidate product design can be an estimate of the cost to manufacture the product using the candidate product design...."; [0074] "... The data can also cause the client-side application to present the scores for each selected candidate product design, e.g., the overall scores, the style scores, the performance scores, the manufacturability score, and/or the design cost....") .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuniavsky to Hurewitz & Fox to consider cost in designing the product, other than sentiment and design consideration (Kuniavsky: [0066]) . Additional motivation to combine would have been that Kuniavsky, Hurewitz & Fox are analogous art to the instant claim in the field of machine learning based product design ( Kuniavsky [0032]; Fox: Abstract; Hurewitz: Abstract).
Regarding Claims 10, 16 & 20
Kuniavsky teaches the method/A computer program product/system of claim 8/12/17 respectively, further comprising:
determining, by the processor set (Kuniavsky: Fig.4 element 410) , a rate of change of product design based on a comparison of secondary product designs generated at consecutive points in time (Kuniavsky: [0072] secondary product design as candidate designs; consecutive points in time as steps of iteration in Fig.3 & [0072]) ;
determining, by the processor set (Kuniavsky: Fig.4 element 410), whether the rate of change of the product design meets a saturation threshold (Kuniavsky: [0072]) ; and
determining, by the processor set (Kuniavsky: Fig.4 element 410), whether to proceed with additional iterations of the generating additional secondary designs of the physical product based on the determining whether the rate of change of the product design meets the saturation threshold(Kuniavsky: [0072] as convergence determination - "...For example, the generative design platform 150 can generate multiple product designs and evaluate the product designs until converging on a set of candidate designs for which information is presented to the user. Convergence can be met when it is determined that changing the characteristics of the candidate product designs do not result in a significant, e.g., at least a threshold, change in the scores between successive iterations. Other convergence conditions can also be used....").
Motivation to combine is similar to claim 4 above and incorporated herein.
Conclusion
All claims are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, March 26, 2026
1 Also see US 20170061454 A1 by Bao; Sheng Hua et al. [0029], [0033] – relevant prior art which may be used in future.
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