DETAILED ACTION
This action is in response to the initial filing of application no. 18/647,092 on 04/26/2024.
Claims 1 - 15 are still pending in this application, with claims 1, 6 and 11 being independent.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Aside from the non-prior art (i.e., ODP and 101) rejections, the prior art fails to teach or suggest in reasonable combination the following limitations recited in claims 3, 8 and 13: for each computing product: identifying electronic documents associated with the computing product; calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using a market prediction model, the market trend data associated with the computing product based on the product sentiment, market data, and financial data results associated with the computing product.
As discussed below with Mishra, Mishra discloses using calculated product sentiment to calculate the market trend data. However, Mishra fails to teach or suggest the use of market data and financial data calculated based on electronic documents to generate the market trend data.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 12 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 12 recites the limitation "the search terms.” There is insufficient antecedent basis for this limitation in the claim.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1,3, 6, 8, 11 and 13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1,3, 6, 8, 11 and 13 of copending Application No. 18/647,123 in view of Mishra (US 11,551,096).
This is a provisional nonstatutory double patenting rejection.
The claim mapping is as follows.
Current Application
1. A computer-implemented method of identifying search terms for an electronic document search engine, comprising: generating, using a market prediction model, market trend data associated with computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; for one or more targeted computing products: iteratively generating, based on the target computing components, a plurality of layouts of the targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
2. The computer-implemented method of claim 1, further including: generating the search terms based on the particular permutated layouts for each of the computing products.
3. The computer-implemented method of claim 1, further including: for each computing product: identifying electronic documents associated with the computing product; calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using a market prediction model, the market trend data associated with the computing product based on the product sentiment, market data, and financial data results associated with the computing product.
4. The computer-implemented method of claim 1, further including: determining, for each of the plurality of permutated layouts of the targeted computing product, a predicted workload of the targeted computing product; and determining, for each of the computing products, a predicted workload of the computing product.
5. The computer-implemented method of claim 4, further including: for each of the computing products: comparing, for each of the plurality of permutated layouts of the targeted computing product, the predicted workload of the targeted computing product with the predicted workload of the computing product; and identifying, based on the comparing, the particular permutated layout that has a greatest difference in predicted workload with the computing product.
6. An information handling system comprising a processor having access to memory media storing instructions executable by the processor to perform operations, comprising: generating, using a market prediction model, market trend data associated with computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; for one or more targeted computing products: iteratively generating, based on the target computing components, a plurality of layouts of the targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
7. The information handling system of claim 6, the operations further including: generating the search terms based on the particular permutated layouts for each of the computing products.
8. The information handling system of claim 6, the operations further including: for each computing product: identifying electronic documents associated with the computing product; calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using a market prediction model, the market trend data associated with the computing product based on the product sentiment, market data, and financial data results associated with the computing product.
9. The information handling system of claim 6, the operations further including: determining, for each of the plurality of permutated layouts of the targeted computing product, a predicted workload of the targeted computing product; and determining, for each of the computing products, a predicted workload of the computing product.
10. The information handling system of claim 9, the operations further including: for each of the computing products: comparing, for each of the plurality of permutated layouts of the targeted computing product, the predicted workload of the targeted computing product with the predicted workload of the computing product; and identifying, based on the comparing, the particular permutated layout that has a greatest difference in predicted workload with the computing product.
11. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: generating, using a market prediction model, market trend data associated with the third-party computing products; generating, using a market prediction model, market trend data associated with computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; for one or more targeted computing products: iteratively generating, based on the target computing components, a plurality of layouts of the targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
12. The non-transitory computer-readable medium of claim 11, the operations further including: generating the search terms based on the particular permutated layouts for each of the computing products.
13. The non-transitory computer-readable medium of claim 11, the operations further including: for each computing product: identifying electronic documents associated with the computing product; calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using a market prediction model, the market trend data associated with the computing product based on the product sentiment, market data, and financial data results associated with the computing product.
14. The non-transitory computer-readable medium of claim 11, the operations further including: determining, for each of the plurality of permutated layouts of the targeted computing product, a predicted workload of the targeted computing product; and determining, for each of the computing products, a predicted workload of the computing product.
15. The non-transitory computer-readable medium of claim 14, the operations further including: for each of the computing products: comparing, for each of the plurality of permutated layouts of the targeted computing product, the predicted workload of the targeted computing product with the predicted workload of the computing product; and identifying, based on the comparing, the particular permutated layout that has a greatest difference in predicted workload with the computing product.
Application no. 18/647,123
1. (Currently Amended) A computer-implemented method of generating targeted computing products, including: training an electronic document crawling model based on a set of electronic documents, including: for each electronic document of the first set of electronic documents: obtaining the electronic document including obtaining an entirety of HyperText Markup Language (HTML) of the electronic document; analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document; and reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes; updating the electronic document crawling model based on the reduced electronic documents; generating, using a market prediction model, market trend data associated with computing products, including: for each computing product: identifying, using the electronic document crawling model, electronic documents associated with computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the identified electronic documents associated with each of the computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; generating, for the targeted computing product, a data table indicating each of the plurality of permutated layouts and each of the combinations of the target features of each of the target
computing components of each of the plurality of permutated layouts; and storing, at [[the]] a storage device, the data table.
2. (Currently Amended) The computer-implemented method of claim 1, further including: for each third-party computing product: identifying, from a data store, a product profile of the computing product, including a list of a plurality of computing components associated with the computing product, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; determining, based on the product profile of the computing product, computational capabilities of the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the computational capabilities of each of the computing products and the identified electronic documents associated with each of the computing products.
3. (Original) The computer-implemented method of claim 2, further including: for each computing product: calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the product sentiment, market data, and financial data results associated with the computing products.
(Currently Amended) An information handling system comprising a processor having access to memory media storing instructions executable by the processor to perform operations, comprising: training an electronic document crawling model based on a set of electronic documents, including :for each electronic document of the first set of electronic documents: obtaining the electronic document including obtaining an entirety of HyperText Markup Language (HTML) of the electronic document; analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document; and reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes; updating the electronic document crawling model based on the reduced electronic documents; generating, using a market prediction model, market trend data associated with computing products, including: for each computing product: identifying, using the electronic document crawling model, electronic documents associated with computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the identified electronic documents associated with each of the computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; generating, for the targeted computing product, a data table indicating each of the plurality of permutated layouts and each of the combinations of the target features of each of the target computing components of each of the plurality of permutated layouts; and storing, at [[the]] a storage device, the data table.
(Currently Amended) The information handling system of claim 1, the operations further including: for each third-party computing product: identifying, from a data store, a product profile of the computing product, including a list of a plurality of computing components associated with the computing product, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component ;determining, based on the product profile of the computing product, computational capabilities of the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the computational capabilities of each of the computing products and the identified electronic documents associated with each of the computing products.
8. (Original) The information handling system of claim 7, the operations further including: for each computing product: calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the product sentiment, market data, and financial data results associated with the computing products.
11. (Currently Amended) A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: training an electronic document crawling model based on a set of electronic documents, including: for each electronic document of the first set of electronic documents: obtaining the electronic document including obtaining an entirety of HyperText Markup Language (HTML) of the electronic document; analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document; and reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes; updating the electronic document crawling model based on the reduced electronic documents; generating, using a market prediction model, market trend data associated with computing products, including :for each computing product: identifying, using the electronic document crawling model, electronic documents associated with computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the identified electronic documents associated with each of the computing products; generating, using a market prediction model, market trend data associated with the computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; generating, for the targeted computing product, a data table indicating each of the plurality of permutated layouts and each of the combinations of the target features of each of the target computing components of each of the plurality of permutated layouts; and storing, at the storage device, the data table.
12. (Currently Amended) The non-transitory computer-readable medium of claim 11, the operations further including: for each third-party computing product: identifying, from a data store, a product profile of the computing product, including a list of a plurality of computing components associated with the computing product, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; determining, based on the product profile of the computing product, computational capabilities of the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the computational capabilities of each of the computing products and the electronic documents associated with each of the computing products.
13. (Original) The non-transitory computer-readable medium of claim 12, the operations further including: for each computing product: calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product; and generating, using the market prediction model, the market trend data associated with the computing products based on the product sentiment, market data, and financial data results associated with the computing products.
As shown above, claims 1, 6 and 11 of application no. 18/647123 recite the limitations of claims 1, 6 and 11 of the current application, respectively, except for the following limitations: identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
However, Mishra discloses an information handling system (Abstract), comprising the following: identifying, from a data store (item information data store, Fig.5, 504), a respective product profile (known feature set) of the computing products (e.g. wireless headphones) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), including a list of a plurality of computing components (e.g. battery) associated with the computing products (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component (e.g. battery life, replaceable) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15); for each computing product: comparing the product profile of the computing product with each of the plurality of layouts (design) of a targeted computing product of the one or more targeted computing products (column 9 lines 4 – 47, 60 – column 10 line 2); identifying, based on the comparing, a particular layout that has a greatest difference in similarity score with the computing product (column 6 lines 19 – 42; column 10 lines 2 – 60); and storing, at the storage device, a table (features combinations data store, Fig.5.508 and Fig.9, 910; column 14 lines 30 -36, column 25 lines 47 – 57, column 26 lines 11 - 15) indicating the particular layout with respect to the computing product (column 14 lines 30 – 36; column 18 lines 17 – 25, 53- 56).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the limitations recited in claims 1, 6 and 11, of application no. 18/647123, respectively, in the same way that Mishra’s invention has been improved to achieve the following, predictable results for the purpose of improving the scalability, efficiency and throughput of a product design process (Mishra, column 1 lines 5 – 17): identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
Since claims 1, 6 and 11 of the current application and claims 1, 6 and 11 of application no. 18/647,123 are obvious variants, these claims are not patentably distinct.
As shown above, claims 3, 8 and 13 of application no. 18/647123 recite the limitations of claims 3, 8, 13 of the current application, respectively, except for the following limitations: identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
However, Mishra discloses an information handling system (Abstract), comprising the following: identifying, from a data store (item information data store, Fig.5, 504), a respective product profile (known feature set) of the computing products (e.g. wireless headphones) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), including a list of a plurality of computing components (e.g. battery) associated with the computing products (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component (e.g. battery life, replaceable) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15); for each computing product: comparing the product profile of the computing product with each of the plurality of layouts (design) of a targeted computing product of the one or more targeted computing products (column 9 lines 4 – 47, 60 – column 10 line 2); identifying, based on the comparing, a particular layout that has a greatest difference in similarity score with the computing product (column 6 lines 19 – 42; column 10 lines 2 – 60); and storing, at the storage device, a table (features combinations data store, Fig.5.508 and Fig.9, 910; column 14 lines 30 -36, column 25 lines 47 – 57, column 26 lines 11 - 15) indicating the particular layout with respect to the computing product (column 14 lines 30 – 36; column 18 lines 17 – 25, 53- 56).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the limitations recited in claims 3, 8 and 13 of application no. 18/647123, respectively, in the same way that Mishra’s invention has been improved to achieve the following, predictable results for the purpose of improving the scalability, efficiency and throughput of a product design process (Mishra, column 1 lines 5 – 17): identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
Since claims 3, 8 and 13 of the current application and claims 3, 8 and 13 of application no. 18/647,123 are obvious variants, these claims are not patentably distinct.
Claims 2, 7 and 12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 6 and 11 of copending Application No. 18/647,123 in view of Mishra (US 11,551,096) and further in view of Kopru et al. (US 2017/037298) (“Kopru”).
As to claims 2, 7 and 12 of the current application, claims 1, 6 and 11 of application no. 18/647,092 recite the limitations of claims 2, 7 and 12, respectively, except for the following: identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product; and generating the search terms based on the particular permutated layouts for each of the computing products.
However, Mishra discloses an information handling system (Abstract), comprising the following: identifying, from a data store (item information data store, Fig.5, 504), a respective product profile (known feature set) of the computing products (e.g. wireless headphones) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), including a list of a plurality of computing components (e.g. battery) associated with the computing products (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component (e.g. battery life, replaceable) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15); for each computing product: comparing the product profile of the computing product with each of the plurality of layouts (design) of a targeted computing product of the one or more targeted computing products (column 9 lines 4 – 47, 60 – column 10 line 2); identifying, based on the comparing, a particular layout that has a greatest difference in similarity score with the computing product (column 6 lines 19 – 42; column 10 lines 2 – 60); and storing, at the storage device, a table (features combinations data store, Fig.5.508 and Fig.9, 910; column 14 lines 30 -36, column 25 lines 47 – 57, column 26 lines 11 - 15) indicating the particular layout with respect to the computing product (column 14 lines 30 – 36; column 18 lines 17 – 25, 53- 56).
Additionally, Kopru discloses a system and method for generating search terms (Abstract), comprising the following: search terms (item vectors which represent words in a product description and additionally received search terms) are generated based on product descriptions ([0036] [0041 – 0047]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the limitations recited in claims 1, 6 and 11 of application no. 18/647123, respectively, in the same way that Mishra’s invention has been improved to achieve the following, predictable results for the purpose of improving the scalability, efficiency and throughput of a product design process (Mishra, column 1 lines 5 – 17): identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product; and storing, at the storage device, a table indicating the particular permutated layout with respect to the computing product.
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of claims 1, 6 and 11 of application no 18/647,092,respectively, and Mishra in the same way that Kopru’s invention has been improved to achieve the following, predictable results for the purpose of improving the system by enabling a user to conduct product searches: search terms are further generated based on the particular product descriptions, e.g. permutated layouts, for each of the computing products.
Since claims 2, 7 and 12 of the current application and claims 1, 6 and 11 of application no. 18/647,123 are obvious variants, these claims are not patentably distinct.
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 – 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Claims 1, 6 and 11 recite the following limitations which are directed to an abstract idea since each limitation can be performed in the mind or by the mind with pencil and paper: generating market trend data associated with computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; for one or more targeted computing products: iteratively generating, based on the target computing components, a plurality of layouts of the targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; identifying, from a data store, a respective product profile of the computing products, including a list of a plurality of computing components associated with the computing products, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; for each computing product: comparing the product profile of the computing product with each of the plurality of permutated layouts of a targeted computing product of the one or more targeted computing products; and identifying, based on the comparing, a particular permutated layout that has a greatest difference in similarity score with the computing product.
Claims 1, 6 and 11 fail to recite limitations which integrate the judicial exception into a practical application. Claim 1, 6 and 11 further recite generating the market trend data using a market prediction model. This limitation is interpreted as generating the market data using a general purpose computer since the market prediction model is recited at a high level of generality. Furthermore, claims 1, 6 and 11 recite the following limitation which is considered extrasolution activity that fails to integrate the judicial exception into a practical application: the storage device, a table indicating the particular permutated layout with respect to the computing product.
Claims 1, 6 and 11 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Using a general purpose computer implement a judicial exception is not sufficient to amount to significantly more than the judicial exception. Additionally, storing data in a storage device would have been well-understood, routine and conventional at the time of applicant’s filing.
Claims 2, 7 and 12 further recite the following limitation which is directed to an abstract idea since the limitation can be performed in the mind or by the mind with pencil and paper: generating the search terms based on the particular permutated layouts for each of the computing products.
Claims 3, 8 and 13 further recite the following limitations which are directed to an abstract idea since each limitation can be performed in the mind or by the mind with pencil and paper: identifying electronic documents associated with the computing product; and generating the market trend data associated with the computing product based on the product sentiment, market data, and financial data results associated with the computing product. Additionally, claims 3, 8, 1nd 13 recite the following limitation which is directed to a mathematical calculation: calculating, based on the electronic documents, product sentiment, market data, and financial data results associated with the computing product.
Claims 3, 8 and 13 fail to recite limitations which integrate the judicial exception into a practical application. Claims 3, 8 and 13 further recite generating the market trend data using a market prediction model. This limitation is interpreted as generating the market data using a general purpose computer since the market prediction model is recited at a high level of generality.
Claims 3, 8 and 13 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Using a general purpose computer implement a judicial exception is not sufficient to amount to significantly more than the judicial exception.
Claims 4, 9 and 14 further recite the following limitations which are directed to an abstract idea since each limitation can be performed in the mind or by the mind with pencil and paper: determining, for each of the plurality of permutated layouts of the targeted computing product, a predicted workload of the targeted computing product; and determining, for each of the computing products, a predicted workload of the computing product.
Claims 5, 10 and 15 further recite the following limitations which are directed to an abstract idea since each limitation can be performed in the mind or by the mind with pencil and paper: comparing, for each of the plurality of permutated layouts of the targeted computing product, the predicted workload of the targeted computing product with the predicted workload of the computing product; and identifying, based on the comparing, the particular permutated layout that has a greatest difference in predicted workload with the computing product.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra (US 11,551,096) in view of Hall et al. (US 2023/0376981) (“Hall”).
For claims 1, 6 and 11, Mishra discloses an information handling system (Abstract), comprising a processor (column 4 lines 18 – 30) having access to memory media (non-transitory media, column 4 lines 18 – 30) storing instructions executable by the processor to perform (column 4 lines 18 – 30) operations, comprising: generating, using a market prediction model (sentiment model, Fig.2, 202), market trend data (scores associated with positive and negative sentiments) associated with computing products (e.g. wireless headphones, sound producing electronics, speakers) (column 4 lines 30 - 63; column 7 lines 54 – column 8 line 40); comparing the market trend data with the product profiles of each of the computing products (A design model accepts sentiments identified by the sentiment model. The design model is trained based on historical feature sets/product profiles which are broadly and reasonably interpreted to include feature sets/profiles of the computing products. The design model compares the identified sentiments to the training data to generate output., column 5 lines 60 – column 6 line 20; column 8 lines 57 – column 9 lines 3, 48 – 59, column 14 lines 30 – 33, 59 – column 15 line 7, column 18 lines 17 - 36); identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products (The design model is broadly and reasonable interpreted to identify all types of target computing components and features of the sentiments/market trend data, including those target components and features that are absent from the feature sets/product profiles of the computing product used to train the model., column 5 lines 60 – column 6 line 20; column 8 lines 57 – column 9 lines 3, 48 – 59); for one or more targeted computing products: iteratively generating, based on the target computing components, a plurality of layouts of the targeted computing product (Fig.1, 114; column 5 lines 60 – column 6 line 18, column 9 lines 48 – 59); identifying, from a data store (item information data store, Fig.5, 504), a respective product profile (known feature set) of the computing products (e.g. wireless headphones) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), including a list of a plurality of computing components (e.g. battery) associated with the computing products (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15), wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component (e.g. battery life, replaceable) (column 14 lines 10 – 33; column 16 lines 65 – column 17 lines 15); for each computing product: comparing the product profile of the computing product with each of the plurality of layouts (design) of a targeted computing product of the one or more targeted computing products (column 9 lines 4 – 47, 60 – column 10 line 2); identifying, based on the comparing, a particular layout that has a greatest difference in similarity score with the computing product (column 6 lines 19 – 42; column 10 lines 2 – 60); and storing, at the storage device, a table (features combinations data store, Fig.5.508 and Fig.9, 910; column 14 lines 30 -36, column 25 lines 47 – 57, column 26 lines 11 - 15) indicating the particular layout with respect to the computing product (column 14 lines 30 – 36; column 18 lines 17 – 25, 53- 56).
Yet, Mishra fails to teach further permutating the layouts, wherein the comparison and identification steps involving the layout further utilize the permutated layouts.
However, Hall discloses a system and method for predicting product performance (Abstract), comprising the following: wherein product data utilized by a model is further permutated ([0041] [0042] [0055] [0096 – 0098]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Mishra’s invention in the same way that Hall’s invention has been improved to achieve the following predictable results for the purpose of improving the scalability, efficiency and throughput of a product design process (Mishra, column 1 lines 5 – 17): the layouts (product data) is further permutated, wherein the comparison and identification steps involving the layout further utilize the permutated layouts.
Claim(s) 2, 7 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over in Mishra (US 11,551,096) in view of Hall et al. (US 2023/0376981) (“Hall”) and further in view of Kopru et al. (US 2017/0372398) (“Kopru”).
For claims 2, 7 and 12, the combination of Mishra and Hall fails to teach, generating the search terms based on the particular permutated layouts for each of the computing products.
However, Kopru discloses a system and method for generating search terms (Abstract), comprising the following: search terms (item vectors which represent words in a product description and additionally received search terms) are generated based on product descriptions ([0036] [0041 – 0047]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Mishra and Hall in the same way that Kopru’s invention has been improved to achieve the following, predictable results for the purpose of improving the system by enabling a user to conduct product searches: search terms are further generated based on the particular product descriptions, e.g. permutated layouts, for each of the computing products.
Claim(s) 4, 5, 9, 10, 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over in Mishra (US 11,551,096) view of Hall et al. (US 2023/0376981) (“Hall”) and further in view of Moustafa et al. (US 2016/0191594).
For claims 4, 9 and 14, the combination of Mishra and Hall fails to teach the following: determining, for each of the plurality of permutated layouts of the targeted computing product, a predicted workload of the targeted computing product; and determining, for each of the computing products, a predicted workload of the computing product.
However, Moustafa discloses a system and method for processing content in a client device (Abstract), wherein the client device comprises features processor workload ([0042] [0043] [0045] [0046] [0047]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Mishra and Hall in the same way that Moustafa’s invention has been improved to achieve the following, predictable results for the purpose of improving the scalability, efficiency and throughput of a product design process (Mishra, column 1 lines 5 – 17): determining, for each of the plurality of permutated layouts of the targeted computing product, a feature including a predicted workload (processor workload) of the targeted computing product; and determining, for each of the computing products, a feature including a predicted workload (processor workload) of the computing product.
For claims 5, 10 and 15, Mishra, Hall and Moustafa further disclose: for each of the computing products: comparing, for each of the plurality of permutated layouts of the targeted computing product, the predicted workload of the targeted computing product with the predicted workload of the computing product (Mishra, There is single target component and feature, column 9 lines 4 – 47, 60 – column 10 line 2) (Hall, [0041] [0042] [0055] [0096 – 0098]) (Moustafa, [0042] [0043] [0045] [0046] [0047]); and identifying, based on the comparing, the particular permutated layout that has a greatest difference in predicted workload with the computing product (Mishra, column 6 lines 19 – 42; column 10 lines 2 – 60) (Hall, [0041] [0042] [0055] [0096 – 0098]) (Moustafa, [0042] [0043] [0045] [0046] [0047]).
Conclusion
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/SONIA L GAY/ Primary Examiner, Art Unit 2657