DETAILED ACTION
This office action is made final. Claims 1-18 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Applicant’s amendment date 08/26/2025, amended claims 1-8, 10-13, and 15, and 17-18.
Response to Amendment
The previously pending rejection to claims 1-18, under 35 USC 101 (Alice), will be maintained.
The previously pending rejection to claims 1-16, under 35 USC 101 (Software Per Se), will be maintained.
The previously pending rejection to claim 18, under 35 USC 101 (Signal Per Se), is withdrawn.
The previously pending claim objection 15 is withdrawn.
Response to Arguments
Applicant’s arguments received on date 08/26/2025 have been fully considered, but they are not persuasive. Moreover, any new grounds of rejection have been necessitated by Applicant's amendments to the claims. The art rejection has been updated to address these amendments.
Response to Arguments under 35 USC 101:
Applicant asserts that “the claim as a whole integrates the recited judicial exception into a practical application of that exception.” Examiner respectfully disagrees.
As discussed above, under the second prong of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55.
Here, under the second prong of Step 2A, the only additional elements beyond the recited abstract idea of claim 1, and similarly claims 17-18, are the recitations of “performs, in reference to a text in which a characteristic of an evaluation target is described, flexible detailed evaluation of the evaluation target for a predetermined goal including multiple sub goals, wherein the evaluation section inputs, a feature amount extracted from a text in which a characteristic of a comparison target evaluated to satisfy any one of predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals of the predetermined goal is described, a feature amount extracted from the text in which the characteristic of the evaluation target is described, to acquire a similarity between the evaluation target and the comparison target for each of the sub goals, evaluates, in reference to the similarity between the evaluation target and the comparison target for each of the sub goals and an ideal pattern for each of the predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals, a similarity between the ideal pattern for each of the predetermined evaluation criteria and the evaluation target, repeats the input of the feature amount and the evaluation of the similarity for different predetermined criteria to generate different similarities for the different predetermined criteria are carried out by at least one computing device,” and these additional elements, individually and in combination, are nothing more than computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Accordingly, contrary to Applicant’s assertions, the judicial exception is not integrated into a practical application under the second prong of Step 2A.
Response to Arguments under 35 USC 103:
Applicant's arguments with respect to the claim rejections have been considered, but are moot in view of the new ground(s) of rejection set forth below in this Office action. The art rejection has been updated to address these amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1 recites, “circuitry.” As best understood, it appears that there is no support for the underlined recitation in the original disclosure of the present application for this limitation. Moreover, none of the drawings shows this particular feature. Therefore, this limitation of claim 1 considered to be new matter. Appropriate correction is required.
Claims 2-16 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claim 1.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-18 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1 and 17-18 recite an abstract idea. Claims 1 and 17-18 include “performs, in reference to a text in which a characteristic of an evaluation target is described, flexible detailed evaluation of the evaluation target for a predetermined goal including multiple sub goals, wherein the evaluation section inputs, a feature amount extracted from a text in which a characteristic of a comparison target evaluated to satisfy any one of predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals of the predetermined goal is described, a feature amount extracted from the text in which the characteristic of the evaluation target is described, to acquire a similarity between the evaluation target and the comparison target for each of the sub goals, evaluates, in reference to the similarity between the evaluation target and the comparison target for each of the sub goals and an ideal pattern for each of the predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals, a similarity between the ideal pattern for each of the predetermined evaluation criteria and the evaluation target, repeats the input of the feature amount and the evaluation of the similarity for different predetermined criteria to generate different similarities for the different predetermined criteria”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for performing evaluation of an evaluation target for a certain goal. As a result, claims 1 and 17-18 recite an abstract idea under Step 2A Prong One.
Claims 2-16 further describe the process for performing evaluation of an evaluation target for a certain goal. As a result, claims 2-16 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1 and 17-18.
With respect to Step 2A Prong Two of the framework, claims 1 and 17-18 do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 17-18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 17-18 include a circuitry, first and second classifiers generated by supervised learning and a program for causing a computer. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1 and 17-18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-16 do not include any additional elements beyond those recited with respect to claims 1 and 17-18. As a result, claims 2-16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1 and 17-18.
With respect to Step 2B of the framework, claims 1 and 17-18 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1 and 17-18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 17-18 include a circuitry, first and second classifiers generated by supervised learning and a program for causing a computer. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1 and 17-18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-16 do not include any additional elements beyond those recited with respect to claims 1 and 17-18. As a result, claims 2-16 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1 and 17-18.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Software per se
Claims 1-16 are further rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. A system or apparatus defined merely by software, or terms synonymous with software or files, represents functional descriptive material (e.g. data structures or software) per se. Such material is considered non-statutory when claimed without appropriate corresponding structure. Here, in the broadest reasonable interpretation consistent with the specification, the Applicant’s claim 1 an information processing apparatus: “an evaluation section” and “classifier generated by supervised learning” encompass functions that can be executed entirely as software per se. As currently written, the claimed system lacks structure and is therefore non-statutory. Accordingly, claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 2-16 also recite elements that encompass functions that can be executed entirely as software per se. As a result, claims 2-16 are similarly rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10-12, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle et al. (US Pub No. 2018/0349795) (hereinafter Boyle et al.) in view of Apte et al. (US Pub No. 2017/0109680) (hereinafter Apte et al.).
Regarding claims 1 and 17-18, Boyle discloses an information processing apparatus comprising circuitry (see Boyle, para [0020], wherein one or more devices, circuits, and/or processing cores configured to process data) that:
performs, in reference to a text in which a characteristic of an evaluation target is described, flexible detailed evaluation of the evaluation target for a predetermined goal including multiple sub goals (see Boyle, para [0027], wherein machine learning to modify an existing base option/product to optimize an optimization goal (i.e., a flexible evaluation is designed to be modified based on new information); para [0041], wherein perform well for a selected metric of an overall optimization goal. The evaluation metrics may correspond to one or more machine learning models that quantify an evaluation metric value for different sets of features to evaluate whether replacement/addition of the alternative feature in the set of features better achieves an optimization goal; para [0060], wherein text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences; and para [0090], wherein the optimization goal may describe the design and/or performance goals for a product…… The goal components may have target segments such as target business line (e.g., women, men, children), target product type (e.g., blouse, dress, pants), client segment, seasonality (e.g., Spring/Summer, Fall/Winter), etc.),
wherein the circuitry
inputs, to a first (categorized) generated by supervised learning that uses a feature amount extracted from a text in which a characteristic of a comparison target evaluated to satisfy any one of predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals of the predetermined goal is described, a feature amount extracted from the text in which the characteristic of the evaluation target is described, to acquire a similarity between the evaluation target and the comparison target for each of the sub goals (see Boyle, para [0043], wherein machine learning models may include trained models generated from a machine learning process such as the process of FIG. 3. Trained models may be categorized by type such as sales models, inventory models, variety models, etc.; para [0059], wherein at 304, supervised machine learning features and parameters are selected; para [0060], wherein a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning; para [0030], wherein generalizations about groups of customers may be made from individual customer attributes. Customers may be grouped by any characteristic, including gender, body type, shared preference (e.g., a measure of similarity between clients such as clients' objective or subjective attributes or learned similarity in product preferences); and para [0091], wherein the optimization goal allows base options to be compared with each other. For example, "I want to increase profits" may correspond to a goal to increase the sales rate and increase selection rate. In various embodiments, one or more goal components may be provided by a user. For example, the user may select from among several goal component options), and
evaluates, in reference to the similarity between the evaluation target and the comparison target for each of the sub goals and an ideal pattern for each of the predetermined evaluation criteria defined by a combination of respective ones of the multiple sub goals, a similarity between the ideal pattern for each of the predetermined evaluation criteria and the evaluation target (see Boyle, para [0030], wherein generalizations about groups of customers may be made from individual customer attributes. Customers may be grouped by any characteristic, including gender, body type, shared preference (e.g., a measure of similarity between clients such as clients' objective or subjective attributes or learned similarity in product preferences); and para [0091], wherein the optimization goal allows base options to be compared with each other. For example, "I want to increase profits" may correspond to a goal to increase the sales rate and increase selection rate. In various embodiments, one or more goal components may be provided by a user. For example, the user may select from among several goal component options; and paras [0059] & [0104], wherein supervised machine learning features and parameters are selected. For example, a user may set control parameters for various machine algorithms to be used to train a model. The selection of the features refers to the selection of machine learning features or individual identifiable properties of an item. The features and parameters may be selected based on objectives for the trained model. Examples of features for a garment product include type (e.g., blouse, dress, pants), silhouette (e.g., a shape of the garment), print (e.g., a pattern on a fabric), material, hemline, sleeve, etc. Examples of features are described with respect to FIG. 8. Identification of features may be received. The selection of features to be utilized in prediction models can be defined at least in part by a human user or at least in part by automatically being determined. For example, a human or artificial intelligence may define features of the prediction models to be trained),
wherein the circuitry repeats the input of the feature amount and the evaluation of the similarity for different predetermined criteria to generate different similarities for the different predetermined criteria (see Boyle, para [0086], wherein 406-414 may be repeated where the base option is the base option with the incorporation of the selected alternative feature(s). This may yield a sub-set or different set of alternative features for incorporation into the base option with the first incorporated alternative feature; para [0068], wherein a trained sales/success model may predict the performance/success of a set of features combined in a product. For example, given two sets of features differing only in color. Because the trained model is able to attribute success to a particular alternative feature or combination of alternative features, the predictions by the trained model may be used to identify alternative features to be included in a product to best match the optimization goal allows a user to view various different output generated from the optimization goal; and paras [0083] & [0113]-[0114], wherein the ordered list may be generated based on the prediction values by sorting at least a portion of the alternative features to generate an ordered list of at least the portion of the alternative features for the selected base option desirability of the different alternative features for the selected base option with respect to the optimization goal determined in 412 are sorted and ranked and the corresponding alternative features are provided in an order list of recommendations (e.g., from best to worst) of alternative features to be utilized to modify the design of the selected base option).
Boyle et al. fails to explicitly disclose inputs, to a first classifier.
Analogous art Apte discloses inputs, to a first classifier (see Apte, abstract, wherein first and second classifiers are trained using goal description and self-comments……..; and para [0028], wherein semi-supervised learning approaches are used that uses both labelled as well as unlabeled data for training of classifiers).
Boyle directed to a system for optimizing computer machine learning includes receiving an optimization goal. Apte directed to identifying labeled and unlabeled goals associated with a role. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Boyle, regarding the System for Using Artificial Intelligence to Design a Product, to have included inputs, to a first classifier because both inventions teach improve accuracy. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Boyle discloses the information processing apparatus according to Claim 1, wherein
the circuitry section performs rating of the evaluation target for the predetermined goal in reference to the similarity between the ideal pattern for each of the predetermined evaluation criteria and the evaluation target (see Boyle, para [0039], wherein information about an item may be stored with one or more associated ratings such as style rating (e.g., a measure of customer satisfaction with a style of an item), size rating (e.g., a measure of an accuracy of the identified size of an item), fit rating (e.g., a measure of customer satisfaction with how well the item fits), quality rating (e.g., a measure of customer satisfaction with quality of an item), retention measure (e.g., a measure of a likelihood that a product leads to a future purchase by a customer), personalization measure (e.g., a measure of customer satisfaction with how well an item matches a customer's personality and uniqueness), style grouping measure (e.g., a likelihood that an item is categorized in a particular group), price value rating (e.g., a rating of a value of the item with respect to its price), and the like. In various embodiments, information about an item may be stored that scores the style with an aggregate metric that represents appropriate weighting/value of any or all of the preceding metrics together).
Regarding claim 3, Boyle discloses the information processing apparatus according to Claim 1, wherein
the circuitry section extracts, from the text in which the characteristic of the evaluation target is described, a sentence that has contributed to improvement of the similarity between the evaluation target and the comparison target for each of the sub goals (see Boyle, para [0060], wherein a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning).
Regarding claim 4, Boyle discloses the information processing apparatus according to claim 3, wherein the circuitry further outputs a result of the evaluation (see Boyle, para [0065]-[0066], wherein in supervised learning, the objective is to determine a weight of a feature in a function that optimizes a desired result, where the function is a representation of the relationship between the features. In a training process, weights associated with features of a model are determined via the training. That is, the contribution of each feature to a predicted outcome of the combination of features is determined…….the output of the function becomes closer to a target or validation result).
Regarding claim 5, Boyle discloses the information processing apparatus according to claim 4, wherein
the circuitry displays, in a form of a list, sentences that have contributed to improvement of the similarity between the evaluation target and the comparison target for each of the sub goals (see Boyle, paras [0083]-[0088], wherein the representation of the product may be displayed alongside the base option to allow for comparison. Examples of visual representations of a product are shown in FIGS. 9 and 16…….The ordered list may be generated based on the prediction values by sorting at least a portion of the alternative features to generate an ordered list of at least the portion of the alternative features for the selected base option. Values (e.g., scores) associated with desirability of the different alternative features for the selected base option with respect to the optimization goal determined in 412 are sorted and ranked and the corresponding alternative features are provided in an order list of recommendations (e.g., from best to worst) of alternative features to be utilized to modify the design of the selected base option. For example, the top ten scoring alternative features or combination of alternative features may be output in a ranked list).
Regarding claim 6, Boyle discloses the information processing apparatus according to claim 4, wherein
the circuitry displays, in a highlighted manner, in the text in which the characteristic of the evaluation target is described, a sentence that has contributed to improvement of the similarity between the evaluation target and the comparison target for each of the sub goals (see Boyle, paras [0106]-[0129], wherein referring to Figs. 7-9 shows a user input regarding an optimization goal (the input fields are collectively referred to as input section 702), output fields providing one or more responses based on the received input (the output fields are collectively referred to as output section 704), and navigation menu 730…..optimization goal includes components: business line (e.g., women, men, children), product type (e.g., blouses, dressed, pants), client segment, fiscal quarter or seasonality (e.g., Spring/Summer, Fall/Winter), and silhouette (e.g., a shape of the garment). The goal components may be selected via drop-down menus as shown or by other input methods such as text entry, selection of a button, and the like. The goal components may be pre-populated with a default value).
Regarding claim 7, Boyle discloses the information processing apparatus according to Claim 1, wherein
the circuitry performs time series evaluation of the evaluation target for the predetermined goal (see Boyle, para [0090], wherein the goal components may have target segments such as target business line (e.g., women, men, children), target product type (e.g., blouse, dress, pants), client segment, seasonality (e.g., Spring/Summer, Fall/Winter), etc. The optimization goal may be evaluated for segments of an optimization type, e.g., optimizes sales for target customers of a certain client segment and in a target product season).
Regarding claim 10, Boyle discloses the information processing apparatus according to claim 7, wherein
the circuitry section performs the time series evaluation in reference to the text in which the characteristic of the evaluation target is described and a text in which a comment of a third party on the evaluation target is described (see Boyle, paras [0090] & [0106]-[0129], wherein referring to Figs. 7-9 shows a user input regarding an optimization goal (the input fields are collectively referred to as input section 702), output fields providing one or more responses based on the received input (the output fields are collectively referred to as output section 704), and navigation menu 730…..optimization goal includes components: business line (e.g., women, men, children), product type (e.g., blouses, dressed, pants), client segment, fiscal quarter or seasonality (e.g., Spring/Summer, Fall/Winter), and silhouette (e.g., a shape of the garment). The goal components may be selected via drop-down menus as shown or by other input methods such as text entry, selection of a button, and the like. The goal components may be pre-populated with a default value; para [0060], wherein text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning; and para [0028], wherein the information may be collected through third party apps or platforms such as apps that allow a user to indicate interests and/or share interest in products with other users. Customer attributes may be collected when a customer enrolls with the system. For example, the customer may complete a survey about his or her measurements (height, weight, etc.), lifestyle, and preferences).
Regarding claim 11, Boyle discloses the information processing apparatus according to Claim 1, wherein
the circuitry determines, in reference to a text in which a characteristic of a screening target is described, whether or not the screening target is appropriate as the evaluation target (see Boyle, para [0060], wherein text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning; and para [0072], wherein the optimization goal may be evaluated for segments of an optimization type, e.g., optimizes sales for target customers of a certain client segment and in a target product season. The optimization goal may be received via a GUI; and para [0109], wherein GUI shown in Fig. 10, a user may select a target client segment by clicking on/touching a target client segment ("Under 30," "30-50," or "50 and over"). A user may select a season by clicking on/touching a season ("Ql," "Q2," "Q3," "Q4")).
Regarding claim 12, Boyle discloses the information processing apparatus according to claim 11, wherein
the circuitry acquires a similarity between the comparison target and the screening target for each of the predetermined evaluation criteria by inputting (see Boyle, paras [0091] & [0126], wherein the optimization goal allows base options to be compared with each other. For example, "I want to increase profits" may correspond to a goal to increase the sales rate and increase selection rate. In various embodiments, one or more goal components may be provided by a user. For example, the user may select from among several goal component options),
to a second (categorized) generated by supervised learning that uses the feature amount extracted from the text in which the characteristic of the comparison target evaluated to satisfy any one of the predetermined evaluation criteria is described, a feature amount extracted from the text in which the characteristic of the screening target is described (see Boyle, para [0043], wherein machine learning models may include trained models generated from a machine learning process such as the process of FIG. 3. Trained models may be categorized by type such as sales models, inventory models, variety models, etc.; para [0059], wherein at 304, supervised machine learning features and parameters are selected; para [0060], wherein a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning; para [0030], wherein generalizations about groups of customers may be made from individual customer attributes. Customers may be grouped by any characteristic, including gender, body type, shared preference (e.g., a measure of similarity between clients such as clients' objective or subjective attributes or learned similarity in product preferences); and para [0091], wherein the optimization goal allows base options to be compared with each other. For example, "I want to increase profits" may correspond to a goal to increase the sales rate and increase selection rate. In various embodiments, one or more goal components may be provided by a user. For example, the user may select from among several goal component options), and
determines, in reference to the similarity, whether or not the screening target is appropriate as the evaluation target (see Boyle, para [0060], wherein text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning; and para [0072], wherein the optimization goal may be evaluated for segments of an optimization type, e.g., optimizes sales for target customers of a certain client segment and in a target product season. The optimization goal may be received via a GUI; and para [0109], wherein GUI shown in Fig. 10, a user may select a target client segment by clicking on/touching a target client segment ("Under 30," "30-50," or "50 and over"). A user may select a season by clicking on/touching a season).
Boyle et al. fails to explicitly disclose a second classifier.
Analogous art Apte discloses a second classifier (see Apte, abstract, wherein first and second classifiers are trained using goal description and self-comments……..; and para [0028], wherein semi-supervised learning approaches are used that uses both labelled as well as unlabeled data for training of classifiers).
Boyle directed to a system for optimizing computer machine learning includes receiving an optimization goal. Apte directed to identifying labeled and unlabeled goals associated with a role. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Boyle, regarding the System for Using Artificial Intelligence to Design a Product, to have included a second classifier because both inventions teach improve accuracy. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17 rejected based upon the same rationale as the rejection of claim 1, respectively, since it is the method claim corresponding to the apparatus claim.
Regarding claim 18 rejected based upon the same rationale as the rejection of claim 1, respectively, since it is the non-transitory computer-readable medium storing computer-readable claim corresponding to the apparatus claim. Claim 18 discloses additional feature a non-transitory computer-readable medium storing computer-readable causing a computer to perform a method (see Boyle, para [0139]).
Claims 8 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle et al. (US Pub No. 2018/0349795) (hereinafter Boyle et al.) in view of Apte et al. (US Pub No. 2017/0109680) (hereinafter Apte et al.), and further in view of Senturk Doganaksoy et al. (US Pub No. 2007/0136115) (hereinafter Senturk Doganaksoy et al.).
Regarding claim 8, Boyle discloses the information processing apparatus according to claim 7.
Boyle et al. and Apte et al. combined fail to explicitly disclose the circuitry detects a predetermined anomalous pattern relating to evaluation of the evaluation target, in reference to the time series evaluation.
Analogous art Senturk discloses the evaluation section detects a predetermined anomalous pattern relating to evaluation of the evaluation target, in reference to the time series evaluation (see Senturk, para [0045], wherein analyzing the dataset to detect anomalous patterns in the dataset via an anomaly detection technique, as indicated at step 44. The anomaly detection techniques may include at least one of outlier detection, trend analysis, correlation analysis, regression analysis, and factor and cluster analysis. Outlier detection statistically measures whether a financial measure associated with the business entity is significantly "high" or "low." and ; para [0050], wherein……the techniques described above may be applied to evaluate various datasets such as financial datasets, demographic datasets, behavioral datasets or census datasets. Additionally, by employing the techniques described in the various embodiments discussed above, the type of statistical models that can be effectively used increases from a few limited choices (e.g., time-varying coefficient survival model, time series model)………..).
Boyle directed to a system for optimizing computer machine learning includes receiving an optimization goal. Senturk Doganaksoy directed to generating multivariate parameters to capture statistical patterns over time. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Boyle, regarding the System for Using Artificial Intelligence to Design a Product, to have included the evaluation section detects a predetermined anomalous pattern relating to evaluation of the evaluation target, in reference to the time series evaluation because both inventions teach acquiring the statistical patterns over time. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, Boyle discloses the information processing apparatus according to Claim 1, wherein the evaluation target includes a (purchase) instrument (see Boyle, para [0029], wherein the customer makes purchases and provides feedback on products, customer attributes may be updated).
Boyle et al. and Apte et al. combined fail to explicitly disclose a financial instrument.
Analogous art Senturk discloses the evaluation target includes a financial instrument (see Senturk, para [0003], wherein examples of quantitative financial data include financial statement reports, stock price and volume, credit and debt ratings and risk scores related to the business entity………..).
One of ordinary skill in the art would have recognized that applying the known technique of Senturk would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Regarding claim 14, Boyle discloses the information processing apparatus according to claim 13, wherein
the text in which the characteristic of the evaluation target is described includes information concerning the (purchase) instrument or an issuer of the financial instrument (see Boyle, para [0060], wherein text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences; and para [0029], wherein the customer makes purchases and provides feedback on products, customer attributes may be updated).
Boyle et al. and Apte et al. combined fail to explicitly disclose information concerning the financial instrument.
Analogous art Senturk discloses information concerning the financial instrument (see Senturk, para [0003], wherein examples of quantitative financial data include financial statement reports, stock price and volume, credit and debt ratings and risk scores related to the business entity………..).
One of ordinary skill in the art would have recognized that applying the known technique of Senturk would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Boyle et al. (US Pub No. 2018/0349795) (hereinafter Boyle et al.), in view of Apte et al. (US Pub No. 2017/0109680) (hereinafter Apte et al.), in view of Senturk Doganaksoy et al. (US Pub No. 2007/0136115) (hereinafter Senturk Doganaksoy et al.), and further in view of N Blower et al. (Outsiders in Red Rock Country: The Kaiparowits Project and the Reputation of American Environmentalism) UNIVERSITY OF KENT- 2018 - search.proquest.com (hereinafter Blower et al.).
Regarding claim 9, Boyle discloses the information processing apparatus according to claim 8.
Boyle et al., Apte et al., and Senturk Doganaksoy et al. combined fail to explicitly disclose the predetermined anomalous pattern includes greenwash.
Analogous art Blower discloses the predetermined anomalous pattern includes greenwash (see Blower, page 215, wherein the New York Times noted that ‘not since the trust-busting days of Theodore Roosevelt has the force of public opinion intruded so emphatically on the business community’s patterns of operation.’63 Electric utilities, withering under a hostile public gaze, turned to the public relations industry and the construction of corporate environmental reputation to evade further regulation. Possessing an environmentally-friendly reputation for utilities and other industrial sectors was not only seen as valuable; it became a commodity in what was a competitive, hostile marketplace; and page 156, wherein engaging in an early example of what would later be termed ‘greenwashing,’ a 1970 copy of industry trade magazine Electrical World conversely attempted to brand utilities as not only environmentally conscious, but intrinsically ecological in their fundamental composition..).
Boyle directed to a system for optimizing computer machine learning includes receiving an optimization goal. Blower Doganaksoy directed to generating multivariate parameters to capture statistical patterns over time. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Boyle, regarding the System for Using Artificial Intelligence to Design a Product, to have included the predetermined anomalous pattern includes greenwash because both inventions teach improve corporate reputation. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle et al. (US Pub No. 2018/0349795) (hereinafter Boyle et al.) in view of Apte et al. (US Pub No. 2017/0109680) (hereinafter Apte et al.), and further in view of Anno Accademico et al. (Using Block Chain Technologies to Promote Sustainability And Efficiency in the Electricity Sector) - 2019/2020, LUISS-Dipartimento di Impresa e Management (hereinafter Accademico et al.).
Regarding claim 15, Boyle discloses the information processing apparatus according to Claim 1.
Boyle et al. and Apte et al. combined fail to explicitly disclose the predetermined goal includes Sustainable Development Goals SDGs.
Analogous art Accademico discloses the predetermined goal includes Sustainable Development Goals SDGs (see Accademico, page 37, wherein an example is the global indicator framework for Sustainable Development Goals (SDG) that was developed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs)………..).
Boyle directed to a system for optimizing computer machine learning includes receiving an optimization goal. Accademico directed to promoting sustainability and efficiency in the electricity sector. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Boyle, regarding the System for Using Artificial Intelligence to Design a Product, to have included the predetermined goal includes Sustainable Development Goals SDGs because both inventions teach achieving the SDGs in every context. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Boyle discloses the information processing apparatus according to claim 15, wherein
Boyle et al. and Apte et al. combined fail to explicitly disclose the predetermined evaluation criteria include a green criterion, a social criterion, and a sustainability criterion.
Analogous art Accademico discloses the predetermined evaluation criteria include a green criterion, a social criterion, and a sustainability criterion (see Accademico, pages 76-79, wherein in local energy circuits it could be possible to trace the origin and the exact path of green energy by certifying its provenance…..The logic is that a prosumer who produces more green energy than he consumes has a surplus of energy that can be sold back to the grid in exchange for a solar energy credit; and page 12, wherein we talk about electricity, we have to consider the term Social Good. Actually, Social Good is a comprehensive term that includes liberal and rightful distribution and environmental sustainability and it applies to services and products that promote the improvement and well-being of individuals, communities and societies…..).
One of ordinary skill in the art would have recognized that applying the known technique of Accademico would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 15.
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 i