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 1-6, 8-21 have been presented for examination based on the amendment filed on 6/30/2026.
Claim 7 is cancelled.
Claim 21 is new.
Claims 1, 6, 8-9, 12, 15, 17 and 19 are amended.
Claim(s) 1-3, 5-6, 8-9, 11-15, 17-19 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.
Claim(s) 21 is 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., further in view of US 11507609 B1 by Warmsley; Dana M. et al.
This action is made Final.
Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.10:
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(Response 1) In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Feedback and sentiment data is shown in Hurewitz as mapped. Fox also shows sentiment data from Fig.2 step 202 and additional feedback data in step 212 both or which are fed into product generation (step 214) and production (step 220).
The newly added limitation is mapped in Fox. No new arguments are made for dependent claims. New claim 21 is rejected under new prior art combination. Examiner respectfully maintains the rejection.
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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 (Updated 9/1/26)
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 [different secondary design] more user-friendly...."); and
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… finalizing, by the processor set, one or more of the different secondary designs of the physical product for one or more of the different groups of users, wherein the finalized one or more of the different secondary designs of the physical product are utilized to initiate production of one or more versions of the physical product, and wherein the finalizing is based on a determination that the user interaction data and the sentiment data associated with the one or more of the different secondary designs satisfy one or more predefined threshold conditions...
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) .
Fox further teaches finalizing (Fox: See Fig.2 element 214 and finalizing as producing the product or service in step 220) , by the processor set, one or more of the different secondary designs of the physical product for one or more of the different groups of users (Fox: [0028] "... [0028] Then, at 212, the cognitive product design program 108A, 108B may receive input associated with a design of a product and/or service. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive input via a user interface that is associated with the cognitive product design program 108A, 108B, whereby the input may include instructions to design a specification for a new product and/or service that may be based on a problem associated with different users, based on one or more parameters, and/or based on a specification submitted by the user via the use interface....") , wherein the finalized one or more of the different secondary designs of the physical product are utilized to initiate production of one or more versions of the physical product (Fox: See Fig.2 Step 220 ;[0039] "... [0039] Thereafter, at 220, the cognitive product design program 108A, 108B may produce the product or service based on the specification....") , and wherein the finalizing is based on a determination that the user interaction data and the sentiment data associated with the one or more of the different secondary designs satisfy one or more predefined threshold conditions (Fox: [0030]-[0038] – where the requirements are gathered, ranked and restricted (threshold ) for age group – [0032] "...Thereafter, based on the received input as well as the user-wide feedback identified and analyzed at step 210, the cognitive product design program 108A, 108B may generate a specification of the product/service that may include one or more functional requirements that are necessary to satisfy the received user input and the identified user-wide feedback...." [0033] "...[0033] Also, according to one embodiment, in generating the specification, the cognitive product design program 108A, 108B may rank the functional requirements based on the user-wide feedback and present the ranked list of functional requirements in the generated specification....", [0036] "...For example, in order to restrict the generated specification or design of the product to a particular age 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 (Updated 9/1/26)
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 (Cancelled), 14 & 18
Hurewitz teaches the method of claim 12/17, 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 (Updated 9/1/26)
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 different 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] "... [0028] Then, at 212, the cognitive product design program 108A, 108B may receive input associated with a design of a product and/or service. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive input via a user interface that is associated with the cognitive product design program 108A, 108B, whereby the input may include instructions to design a specification for a new product and/or service that may be based on a problem associated with different users, based on one or more parameters, and/or based on a specification submitted by the user via the use interface...."; [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 the different groups of users into the ML predictive model based on the additional user interaction data collected over time (Fox: Fig.2 element 212 & [0028] "...[0028] Then, at 212, the cognitive product design program 108A, 108B may receive input associated with a design of a product and/or service. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive input via a user interface that is associated with the cognitive product design program 108A, 108B, whereby the input may include instructions to design a specification for a new product and/or service that may be based on a problem associated with different users, based on one or more parameters, and/or based on a specification submitted by the user via the use interface. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive user input that includes a problem that the user may want to address in the design of a product and/or service. 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. ..." [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 (Updated 9/1/26)
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 physical product (Fox : Fig.2 element 220) comprises: generating, by the processor set, a new digital twin model for each of the one or more different secondary designs (Fox: [0028] Fig.2 element 212 & [0028] "...[0028] Then, at 212, the cognitive product design program 108A, 108B may receive input associated with a design of a product and/or service. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive input via a user interface that is associated with the cognitive product design program 108A, 108B, whereby the input may include instructions to design a specification for a new product and/or service that may be based on a problem associated with different users, based on one or more parameters, and/or based on a specification submitted by the user via the use interface. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive user input that includes a problem that the user may want to address in the design of a product and/or service. 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. ...") ; converting, by the processor set, the new digital twin model for each of the one or more different secondary designs to a new virtual digital twin model for each of the one or more different 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 different 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 different 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 the different groups of users into the trained ML predictive model, thereby generating the additional secondary designs of the product for the respective ones of the different 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 it is clear that product design is an iterative process (see 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 (Updated 9/1/26)
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) .
Fox further teaches finalizing (Fox: See Fig.2 element 214 and finalizing as producing the product or service in step 220) , by the processor set, one or more of the different secondary designs of the physical product for one or more of the different groups of users (Fox: [0028] "... [0028] Then, at 212, the cognitive product design program 108A, 108B may receive input associated with a design of a product and/or service. Specifically, according to one embodiment, the cognitive product design program 108A, 108B may receive input via a user interface that is associated with the cognitive product design program 108A, 108B, whereby the input may include instructions to design a specification for a new product and/or service that may be based on a problem associated with different users, based on one or more parameters, and/or based on a specification submitted by the user via the use interface....") , wherein the finalized one or more of the different secondary designs of the physical product are utilized to initiate production of one or more versions of the physical product (Fox: See Fig.2 Step 220 ;[0039] "... [0039] Thereafter, at 220, the cognitive product design program 108A, 108B may produce the product or service based on the specification....") , and wherein the finalizing is based on a determination that the user interaction data and the sentiment data associated with the one or more of the different secondary designs satisfy one or more predefined threshold conditions (Fox: [0030]-[0038] – where the requirements are gathered, ranked and restricted (threshold ) for age group – [0032] "...Thereafter, based on the received input as well as the user-wide feedback identified and analyzed at step 210, the cognitive product design program 108A, 108B may generate a specification of the product/service that may include one or more functional requirements that are necessary to satisfy the received user input and the identified user-wide feedback...." [0033] "...[0033] Also, according to one embodiment, in generating the specification, the cognitive product design program 108A, 108B may rank the functional requirements based on the user-wide feedback and present the ranked list of functional requirements in the generated specification....", [0036] "...For example, in order to restrict the generated specification or design of the product to a particular age 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).
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.
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Claim(s) 21 is 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., further in view of US 11507609 B1 by Warmsley; Dana M. et al.
Regarding Claim 21 (New)
Teachings of Hurewitz, Fox and Kuniavsky are shown in the parent claim 1.
Fox & Kuniavsky teaches finalizing, by the processor set, one or more of the additional secondary designs of the physical product based on (Fox Fig.2 steps 212, 214, 218, 220 and [0030]-[0038] – where the requirements are gathered, ranked and restricted (threshold ) for age group – [0032] "...Thereafter, based on the received input as well as the user-wide feedback identified and analyzed at step 210, the cognitive product design program 108A, 108B may generate a specification of the product/service that may include one or more functional requirements that are necessary to satisfy the received user input and the identified user-wide feedback...." [0033] "...[0033] Also, according to one embodiment, in generating the specification, the cognitive product design program 108A, 108B may rank the functional requirements based on the user-wide feedback and present the ranked list of functional requirements in the generated specification....", [0036] "...For example, in order to restrict the generated specification or design of the product to a particular age group,..."; Kuniavsky: convergence based on design requirement to finalize the product [0072], using sentiment analysis [0066]"... [0066] The user can also provide, as input, additional preferences that can be used in the generative design process... The user's visual preferences can be determined using conjoint analysis, sentiment analysis, and/or genetic algorithms..").
Although Hurewitz, Fox and Kuniavsky teach use of sentiment to design product, they do not teach rate of change of sentiment/additional user input as criteria for product design.
Warmsley teaches the method of claim 9, further comprising: determining, by the processor set, whether a rate of change of the additional user interaction data and the additional sentiment data, relative to the user interaction data and the sentiment data for a prior version of the (Warmsley : discussing sentiment analysis/user interaction Col.6 Lines 45-Col.7 Lines 55, Col.8 Lines 6-67; e.g. Col.8
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) new virtual digital twin model for each of the one or more different secondary designs, is within a predetermined threshold limit (Warmsley: Col.8 Lines 6-67 - Comments related to model A exceeding/falling below the threshold for sentiment; models are products like automobiles - Col.5 Lines 28-58).
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 Warmsley to Kuniavsky-Hurewitz-Fox combination to rate change of sentiment and include the rating in action performed for a product/design under consideration and design consideration (Warmsley: Col.6 Lines 45-Col.7 Lines 55, Col.8 Lines 6-67). Warmsley further details how sentiment analysis (which Kuniavsky-Hurewitz-Fox do not present in as much detail) can influence the actions performed for the product and such sentiment analysis can be time driven (Warmsley: Col.6 Lines 8-24; time series data Col.7 lines 12-32) . Further motivation to combine would have been that Warmsley, Kuniavsky, Hurewitz & Fox are analogous art to the instant claim in the field of sentiment analysis ( Warmsley: interaction Col.6 Lines 45-Col.7 Lines 55, Col.8 Lines 6-67; Kuniavsky [0066]; Fox: [0037], Abstract; Hurewitz: Abstract).
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Relevant Prior Art of Record
US 20130144937 A1 by Lee; Ho-Sub teaches emotion analysis unit configured to calculate an emotion rate defined based on a ratio of one kind of emotional state of the at least two kinds of emotional states to another kind of emotional state of the at least two kinds of emotional states; a change-in-emotion rate calculator configured to calculate a change in the emotion rate. This prior art may be used in future to further teach rate of change of emotion which may be used in already used prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Friday, August 28, 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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