Introduction
This Final Office Action is in response to amendments and remarks filed on December 22, 2025, for the application with serial number 18/647,123.
Claims 1, 2, 6, 7, 11, and 12 are amended.
Claims 1-15 are amended.
Interview
The Examiner acknowledges the interview conducted on December 1, 2025, in which proposed amendments were discussed with respect to the outstanding rejections.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims do not fall within the subcategory of “certain methods of organizing human activity” or “managing personal behavior or relationships or interaction between people.” See Remarks pp. 11-12. The Examiner respectfully disagrees. The present claims recite steps for using, for example, successful and trending web page designs to create a web page. See exemplary independent claim 1: “ 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;” and “iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product.” The steps are logical steps a human being could follow to use existing web page designs as templates to create new web pages. Therefore, the steps of the claims attempt to manage human behavior.
The rejection, below, does not allege that the present claims are directed to a mathematical concept or a mental process; making the Applicant’s arguments with respect to those categories moot.
The Applicant additionally contends that the present claims recite an improvement to the functioning of a computer, or a technology. See Remarks p. 13. The Examiner respectfully disagrees. The present claims essentially recite highly generalized steps for using popular web page designs to design other web pages. Eliminating headers and footers from a document does not improve the functioning of a computer.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §112 Rejections
In light of the Applicant’s amendments the rejection of the independent claims for lack of antecedent basis under 35 USC §112, second paragraph, is withdrawn.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Khandekar reference, which is cited in the rejection of the independent claims, below. The Applicant’s arguments are moot in light of the newly cited reference.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-15 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-15 are all directed to one of the four statutory categories of invention, the claims are directed to generating targeted computing products (as evidenced by the preamble of exemplary independent claim 1), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: [1] “training an electronic document crawling model;” [2] “obtaining . . . HyperText Markup Language [ ] of the electronic document;” “[3] analyzing a copy of the electronic document;” “[4] “identifying a plurality of elements of the electronic document;” “[5] reducing the electronic document;” [6] “updating the electronic document crawling model;” [7] “generating . . . market trend data;” [8] “identifying . . . electronic documents associate with the computing product;” “[9] “generating . . . the market trend data;” [10] “comparing the market trend data with [ ] product profiles;” [11] “identifying . . . target computing components and target features of the market trend data;” [12] “generating, based on the target computing components, a plurality of layouts of a targeted computing product;” [13] “permutating each of the plurality of layouts . . . based on a plurality of combinations of the target features;” [14] “generating . . . a data table indicating each of the plurality of permutated layouts;” and [15] “storing . . . the data table.” Steps [2]-[5] and [7]-[15] are steps for managing personal behavior related to the abstract idea of generating targeted computing products that, when considered alone and in combination, are part of the abstract idea of targeted computing products. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating targeted computing products. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes designing software based on trending and desired features..
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a computer-implemented method with a storage device in independent claim 1; a system with a processor, memory media, and storage device in independent claim 6; and a computer readable medium and storage device in independent claim 11). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). Steps [1] and [6], identified above, do recite steps for applying a document crawling model, but the steps merely amount to the use of web crawling for data input. Therefore, the steps amount to insignificant extrasolution activity. See MPEP §2106.05(g). The claims require no more than a generic computer (a computer-implemented method with a storage device in independent claim 1; a system with a processor, memory media, and storage device in independent claim 6; and a computer readable medium and storage device in independent claim 11) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Furthermore: an element found to amount to insignificant extrasolution activity in step 2A of the subject matter eligibility analysis must be evaluated in step 2B to determine whether the step amounts to more than what is well-understood, routine, and conventional. Steps [1] and [6], identified in the analysis above, are well-understood, routine, and conventional, as evidenced by at least cited abstract, ¶[0011], [0052]. and [0165] of Khandekar. This portions teach extracting text information from HTML documents, which amounts to web crawling, as described in the claims. The claims are directed to an abstract idea without significantly more.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are 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.
Independent claims Claim 1, 6, and 11 recite the limitation "the storage device" in the final limitation. There is insufficient antecedent basis for this limitation in the claim. The dependent claims inherit the deficiency.
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, 5, 6, 10, 11, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210150546 A1 to Zhu et al. (hereinafter ‘ZHU’) in view of US 20210042518 A1 to Khandekar (hereinafter ‘KHANDEKAR’) and US 20230297741 A1 to Karweta et al. (hereinafter ‘KARWETA’).
Claim 1 (Currently Amended)
ZHU discloses a computer-implemented method (see ¶[0009]; a computing system (e.g., a platform) or a device (e.g., a user device) includes one or more processors, and memory storing instruction, the instructions, when executed by the one or more processors, cause the processors to perform operations of any of the methods described herein) of generating targeted computing products (see ¶[0002]-[0003]; design a new product or improve an existing product that meets market demand and customer needs).
ZHU does not specifically disclose, but KHANDEKAR discloses, including: training an electronic document crawling model based on a set of electronic documents (see abstract and ¶[0011]; system training using some form of machine learning. Isolate and extract specific text information),
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 (see ¶[0166]; some documents are HTML pages loaded in Web browsers);
analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document (see abstract and ¶[0014 and [0018]; identify information of interest. Determine the start and end of relevant sections); and
reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document (see ¶[0102 and Fig. 7; delete page footers and headers) and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes (see ¶[0007], [0180]-[0187], and [0214]; a machine learning model taught using examples of unstructured text data layouts, including HTML tag ids, names, or paths);
updating the electronic document crawling model based on the reduced electronic documents (see ¶[0165]; delete the header and footer to extract contiguous information in a page break. See also ¶[0052]; ignore headers and footers);
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 (see again abstract and ¶[0014]; extract specific relevant text information).
The combination of ZHU and KHANDEKAR does not specifically disclose, but KARWETA discloses, associated with computing product (see ¶[0017]-[0020] and [0057]; use a corpus of design data containing historic designs. A file includes a collection of multiple designs for an application or website. A “page” may refer to the design being created).
ZHU further discloses generating, using the market prediction model, the market trend data associated with the computing products (see ¶[0036]; reveal the future trend of a product) based on the identified electronic documents associated with each of the computing products (see ¶[0026]; one or more databases for storing data and models. See also ¶[0002] and [0035]-[0036]; design a new product using extracted topics and sentiment related to a product.);
comparing the market trend data with the product profiles of each of the computing products (see ¶[0030] & [0034] and Fig. 2; attribute clusters for brands and products. See also ¶[0086]; obtain values of a measure of technical development.trend – e.g., old/past/outdated technology vs. future/trending technology);
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 (see ¶[0007], [0032], and [0036]; through mining filing and filed patents, the company can plan what area is still blank or needs to be improved. Identify topics of the products to be considered for development, and sentiments associated with the topics. Focus on using stainless steel.).
ZHU does not specifically disclose, but KARWETA discloses, iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product (see abstract and ¶[0020], [0038], [0046] & [0053], and claims 1 and 3; facilitate generation of a user interface design. Predict next elements of screen design to be added to the screen design. A design project includes multiple designs being develop for an application or website. Recommend groupings or complete forms. Receive training data regarding successful and unsuccessful designs. Learn from most recent and current designs in progress. Enables change to recommendations as the design-build continues and changes. Designers of a subsequent project design, benefit from feedback and learning of current and previous designs);
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 (see ¶[0021], [0027]-[0028], and [0031]; a design file may be part of a design project that includes a combination of files. A design template may be thought of as a pre-determined combination of design elements intended for a particular use and context. Design element library 130 includes levels of element combinations and may include design structures following an atomic design that include atomic level, molecular level, and organism level. The machine learning model includes a convolutional neural network (CNN) in which sets of design elements composing part or all of a design are fed into the CNN. The statistical model resulting from the CNN can be trained on successful designs and the statistical model learns unsuccessful and ineffective designs as well, so that when encountering a design under build, a determination of design adequacy and effectiveness can be made.).
ZHU further discloses 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 (see ¶[0052]; Fig. 5A; identify top attribute clusters); and
storing, at a storage device, the data table (see ¶[0009] and [0024]-[0026]; a memory that stores obtained data).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement using information extracted from relevant fields (see ¶[0036]). KHANDEKAR discloses scanning text data, including html tags, to find relevant information in web pages. It would have been obvious for one of ordinary skill in the art to scan web pages for relevant information as taught by KHANDEKAR in the system executing the method of ZHU with the motivation to extract relevant information and meet needs.
Claim 5 (Original)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the computer-implemented method as set forth in claim 1.
ZHU does not specifically disclose, but KARWETA discloses further including: generating a particular permutated layout having a particular combination of target features of each of the target computing components of the particular permutated layout (see ¶[0002]-[0035]; design assistance program 200 includes a statistical machine learning model trained by data including historical and current designs, published best practices and guidance, consortium standards and trends, aligned by design type, industry, and target audience. Considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen.).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim 6 (Currently Amended)
ZHU discloses an information handling system comprising a processor having access to memory media (see ¶[0009]; a computing system (e.g., a platform) or a device (e.g., a user device) includes one or more processors, and memory storing instruction, the instructions, when executed by the one or more processors, cause the processors to perform operations of any of the methods described herein) storing instructions executable by the processor to perform operations (see again ¶[0009]; memory storing instruction, the instructions, when executed by the one or more processors, cause the processors to perform operations),
ZHU does not specifically disclose, but KHANDEKAR discloses, comprising: training an electronic document crawling model based on a set of electronic documents (see abstract and ¶[0011]; system training using some form of machine learning. Isolate and extract specific text information),
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 (see ¶[0166]; some documents are HTML pages loaded in Web browsers);
analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document (see abstract and ¶[0014 and [0018]; identify information of interest. Determine the start and end of relevant sections); and
reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document (see ¶[0102 and Fig. 7; delete page footers and headers) and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes (see ¶[0007], [0180]-[0187], and [0214]; a machine learning model taught using examples of unstructured text data layouts, including HTML tag ids, names, or paths);
updating the electronic document crawling model based on the reduced electronic documents (see ¶[0165]; delete the header and footer to extract contiguous information in a page break. See also ¶[0052]; ignore headers and footers);
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 (see again abstract and ¶[0014]; extract specific relevant text information).
The combination of ZHU and KHANDEKAR does not specifically disclose, but KARWETA discloses, associated with computing product (see ¶[0017]-[0020] and [0057]; use a corpus of design data containing historic designs. A file includes a collection of multiple designs for an application or website. A “page” may refer to the design being created).
ZHU further discloses, generating, using the market prediction model, the market trend data associated with the computing products (see ¶[0036]; reveal the future trend of a product) based on the identified electronic documents associated with each of the computing products (see ¶[0026]; one or more databases for storing data and models. See also ¶[0002] and [0035]-[0036]; design a new product using extracted topics and sentiment related to a product.);
comparing the market trend data with the product profiles of each of the computing products (see ¶[0030] & [0034] and Fig. 2; attribute clusters for brands and products. See also ¶[0086]; obtain values of a measure of technical development.trend – e.g., old/past/outdated technology vs. future/trending technology);
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 (see ¶[0007], [0032], and [0036]; through mining filing and filed patents, the company can plan what area is still blank or needs to be improved. Identify topics of the products to be considered for development, and sentiments associated with the topics. Focus on using stainless steel.).
ZHU does not specifically disclose, but KARWETA discloses, iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product (see abstract and ¶[0020], [0038], [0046] & [0053], and claims 1 and 3; facilitate generation of a user interface design. Predict next elements of screen design to be added to the screen design. A design project includes multiple designs being develop for an application or website. Recommend groupings or complete forms. Receive training data regarding successful and unsuccessful designs. Learn from most recent and current designs in progress. Enables change to recommendations as the design-build continues and changes. Designers of a subsequent project design, benefit from feedback and learning of current and previous designs);
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 (see ¶[0021], [0027]-[0028], and [0031]; a design file may be part of a design project that includes a combination of files. A design template may be thought of as a pre-determined combination of design elements intended for a particular use and context. Design element library 130 includes levels of element combinations and may include design structures following an atomic design that include atomic level, molecular level, and organism level. The machine learning model includes a convolutional neural network (CNN) in which sets of design elements composing part or all of a design are fed into the CNN. The statistical model resulting from the CNN can be trained on successful designs and the statistical model learns unsuccessful and ineffective designs as well, so that when encountering a design under build, a determination of design adequacy and effectiveness can be made.);
ZHU further discloses, 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 (see ¶[0052]; Fig. 5A; identify top attribute clusters);; and
storing, at a storage device, the data table (see ¶[0009] and [0024]-[0026]; a memory that stores obtained data).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement using information extracted from relevant fields (see ¶[0036]). KHANDEKAR discloses scanning text data, including html tags, to find relevant information in web pages. It would have been obvious for one of ordinary skill in the art to scan web pages for relevant information as taught by KHANDEKAR in the system executing the method of ZHU with the motivation to extract relevant information and meet needs.
Claim 10 (Original)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the information handling system of claim 1 [sic].
ZHU does not specifically disclose, but KARWETA discloses the operations further including: generating a particular permutated layout having a particular combination of target features of each of the target computing components of the particular permutated layout (see ¶[0002]-[0035]; design assistance program 200 includes a statistical machine learning model trained by data including historical and current designs, published best practices and guidance, consortium standards and trends, aligned by design type, industry, and target audience. Considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen.).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim 11 (Currently Amended)
ZHU discloses a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers (see ¶[0009]; a computing system (e.g., a platform) or a device (e.g., a user device) includes one or more processors, and memory storing instruction, the instructions, when executed by the one or more processors, cause the processors to perform operations of any of the methods described herein).
ZHU does not specifically disclose, but KHANDEKAR discloses, 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 (see abstract and ¶[0011]; system training using some form of machine learning. Isolate and extract specific text information),
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 (see ¶[0166]; some documents are HTML pages loaded in Web browsers);
analyzing a copy of the electronic document, including: identifying a plurality of elements of the electronic document (see abstract and ¶[0014 and [0018]; identify information of interest. Determine the start and end of relevant sections); and
reducing the electronic document by i) removing a first set of elements including headers and footers of the electronic document (see ¶[0102 and Fig. 7; delete page footers and headers) and ii) maintaining a second set of elements including HTML tags, text associated with the HTML tags, and HTML attributes (see ¶[0007], [0180]-[0187], and [0214]; a machine learning model taught using examples of unstructured text data layouts, including HTML tag ids, names, or paths);
updating the electronic document crawling model based on the reduced electronic documents (see ¶[0165]; delete the header and footer to extract contiguous information in a page break. See also ¶[0052]; ignore headers and footers);
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 (see again abstract and ¶[0014]; extract specific relevant text information).
The combination of ZHU and KHANDEKAR does not specifically disclose, but KARWETA discloses, associated with computing product (see ¶[0017]-[0020] and [0057]; use a corpus of design data containing historic designs. A file includes a collection of multiple designs for an application or website. A “page” may refer to the design being created).
ZHU further discloses, generating, using the market prediction model, the market trend data associated with the computing products (see ¶[0036]; reveal the future trend of a product); based on the identified electronic documents associated with each of the computing products (see ¶[0026]; one or more databases for storing data and models. See also ¶[0002] and [0035]-[0036]; design a new product using extracted topics and sentiment related to a product.);
comparing the market trend data with the product profiles of each of the computing products (see ¶[0030] & [0034] and Fig. 2; attribute clusters for brands and products. See also ¶[0086]; obtain values of a measure of technical development.trend – e.g., old/past/outdated technology vs. future/trending technology);
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 (see ¶[0007], [0032], and [0036]; through mining filing and filed patents, the company can plan what area is still blank or needs to be improved. Identify topics of the products to be considered for development, and sentiments associated with the topics. Focus on using stainless steel.).
ZHU does not specifically disclose, but KARWETA discloses, iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product (see abstract and ¶[0020], [0038], [0046] & [0053], and claims 1 and 3; facilitate generation of a user interface design. Predict next elements of screen design to be added to the screen design. A design project includes multiple designs being develop for an application or website. Recommend groupings or complete forms. Receive training data regarding successful and unsuccessful designs. Learn from most recent and current designs in progress. Enables change to recommendations as the design-build continues and changes. Designers of a subsequent project design, benefit from feedback and learning of current and previous designs);
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 (see ¶[0021], [0027]-[0028], and [0031]; a design file may be part of a design project that includes a combination of files. A design template may be thought of as a pre-determined combination of design elements intended for a particular use and context. Design element library 130 includes levels of element combinations and may include design structures following an atomic design that include atomic level, molecular level, and organism level. The machine learning model includes a convolutional neural network (CNN) in which sets of design elements composing part or all of a design are fed into the CNN. The statistical model resulting from the CNN can be trained on successful designs and the statistical model learns unsuccessful and ineffective designs as well, so that when encountering a design under build, a determination of design adequacy and effectiveness can be made.).
ZHU further discloses and storing, at the storage device, the data table (see ¶[0009] and [0024]-[0026]; a memory that stores obtained data).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement using information extracted from relevant fields (see ¶[0036]). KHANDEKAR discloses scanning text data, including html tags, to find relevant information in web pages. It would have been obvious for one of ordinary skill in the art to scan web pages for relevant information as taught by KHANDEKAR in the system executing the method of ZHU with the motivation to extract relevant information and meet needs.
Claim 15 (Original)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the non-transitory computer-readable medium of claim 11.
ZHU does not specifically disclose, but KARWETA discloses the operations further including: generating a particular permutated layout having a particular combination of target features of each of the target computing components of the particular permutated layout (see ¶[0002]-[0035]; design assistance program 200 includes a statistical machine learning model trained by data including historical and current designs, published best practices and guidance, consortium standards and trends, aligned by design type, industry, and target audience. Considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen.).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design based on acquired feedback. It would have been obvious for one of ordinary skill in the art at the time of invention to include the feedback and machine learning as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim(s) 2-4, 7-9, and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210150546 A1 to ZHU et al. in view of US 20210042518 A1 to KHANDEKAR and US 20230297741 A1 to KARWETA et al. as applied to claim 1 above, and further in view of US 8621450 B2 to Firman et al. (hereinafter ‘FIRMAN’).
Claim 2 (Currently Amended)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the computer-implemented method as set forth in claim 1.
ZHU does not specifically disclose, but KARWETA discloses, 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 (see ¶[0002], [0018] and [0050]; considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen. An application design may be oriented towards a user purpose, may include collection and presentation of information, and apply to a particular industry and include a style and other features other than the functional aspect of the application. Design assistant program 200 analyzes the design elements added to the artboard and the overall design being built and checks the design and design elements for inclusion of known accessibility standards and opportunities for accessibility feature improvements. Accessibility issues may include selecting colors as a distinguishing characteristic and not accounting for color blindness, or inadequate indicators of user action required or expected. Design assistant program 200 determines absent accessibility features, inadequate or inappropriate accessibility features, and incorrectly applied accessibility features of the in-process design.).
The combination of ZHU, KARWETA, and KHANDEKAR does not specifically disclose, but FIRMAN discloses, determining, based on the product profile of the computing product, computational capabilities of the computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability).
ZHU does not specifically disclose, but FIRMAN discloses, 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 (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Claim 3 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the computer-implemented method as set forth in claim 2.
ZHU additionally discloses further including: for each computing product: calculating, based on the electronic documents (see abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), product sentiment (see again abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), market data (see ¶[0036]; reveal the future trend of a product. See also ¶[0002]; Product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs),
ZHU does not specifically disclose, but KARWETA discloses, and financial data results associated with the computing product (see ¶[0088]; Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources).
ZHU further discloses 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 (see ¶[0002] and [0007]; product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs. The system (e.g., the platform) disclosed herein uses the topic modeling and sentiment analysis to identify topics of the product(s) to be considered for the product research and development, and sentiments associated with the respective topics ).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design that tracks costs. It would have been obvious for one of ordinary skill in the art at the time of invention to include costs as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim 4 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the computer-implemented method as set forth in claim 3.
ZHU does not specifically disclose, but FIRMAN discloses, further including: identifying, from a data store, physical constraints associated with the target computing components of the particular computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability); and
iteratively generating, based on the physical constraints associated with the target computing components, a plurality of layouts of a targeted computing product (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Claim 7 (Currently Amended)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the information handling system as set forth in claim 1.
ZHU does not specifically disclose, but KARWETA discloses, 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 (see ¶[0002], [0018] and [0050]; considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen. An application design may be oriented towards a user purpose, may include collection and presentation of information, and apply to a particular industry and include a style and other features other than the functional aspect of the application. Design assistant program 200 analyzes the design elements added to the artboard and the overall design being built and checks the design and design elements for inclusion of known accessibility standards and opportunities for accessibility feature improvements. Accessibility issues may include selecting colors as a distinguishing characteristic and not accounting for color blindness, or inadequate indicators of user action required or expected. Design assistant program 200 determines absent accessibility features, inadequate or inappropriate accessibility features, and incorrectly applied accessibility features of the in-process design.).
The combination of ZHU, KARWETA, and KHANDEKAR does not specifically disclose, but FIRMAN discloses, determining, based on the product profile of the computing product, computational capabilities of the computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability).
ZHU does not specifically disclose, but FIRMAN discloses, 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 (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Claim 8 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the information handling system as set forth in claim 7.
ZHU additionally discloses the operations further including: for each computing product: calculating, based on the electronic documents (see abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), product sentiment (see again abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), market data (see ¶[0036]; reveal the future trend of a product. See also ¶[0002]; Product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs),
ZHU does not specifically disclose, but KARWETA discloses, and financial data results associated with the computing product (see ¶[0088]; Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources).
ZHU further discloses 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 (see ¶[0002] and [0007]; product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs. The system (e.g., the platform) disclosed herein uses the topic modeling and sentiment analysis to identify topics of the product(s) to be considered for the product research and development, and sentiments associated with the respective topics ).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design that tracks costs. It would have been obvious for one of ordinary skill in the art at the time of invention to include costs as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim 9 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the information handling system as set forth in claim 8.
ZHU does not specifically disclose, but FIRMAN discloses, the operations further including: identifying, from a data store, physical constraints associated with the target computing components of the particular computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability); and
iteratively generating, based on the physical constraints associated with the target computing components, a plurality of layouts of a targeted computing product (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Claim 12 (Currently Amended)
The combination of ZHU, KARWETA, and KHANDEKAR discloses the non-transitory computer-readable medium as set forth in claim 11.
ZHU does not specifically disclose, but KARWETA discloses, 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 (see ¶[0002], [0018] and [0050]; considerations of the component design elements of the application include user interaction with design components, the usability aspects of the elements, the effectiveness of flow sequence, and the recognition of important elements of a screen. An application design may be oriented towards a user purpose, may include collection and presentation of information, and apply to a particular industry and include a style and other features other than the functional aspect of the application. Design assistant program 200 analyzes the design elements added to the artboard and the overall design being built and checks the design and design elements for inclusion of known accessibility standards and opportunities for accessibility feature improvements. Accessibility issues may include selecting colors as a distinguishing characteristic and not accounting for color blindness, or inadequate indicators of user action required or expected. Design assistant program 200 determines absent accessibility features, inadequate or inappropriate accessibility features, and incorrectly applied accessibility features of the in-process design.).
The combination of ZHU, KARWETA, and KHANDEKAR does not specifically disclose, but FIRMAN discloses, determining, based on the product profile of the computing product, computational capabilities of the computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability); 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 (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Claim 13 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the non-transitory computer-readable medium of claim 12
ZHU additionally discloses the operations further including: for each computing product: calculating, based on the electronic documents (see abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), product sentiment (see again abstract and ¶[0038]; perform sentiment analysis on product-specific data. Obtain various types of data obtained from data sources, including technical documents such as patent database), market data (see ¶[0036]; reveal the future trend of a product. See also ¶[0002]; Product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs),
ZHU does not specifically disclose, but KARWETA discloses, and financial data results associated with the computing product (see ¶[0088]; Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources).
ZHU further discloses, 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 (see ¶[0002] and [0007]; product planning and optimization includes designing a new product or improving an existing product that meet market demand and customer needs. The system (e.g., the platform) disclosed herein uses the topic modeling and sentiment analysis to identify topics of the product(s) to be considered for the product research and development, and sentiments associated with the respective topics ).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. KARWETA discloses a design assistant that uses machine learning to recommend design builds for user interface design that tracks costs. It would have been obvious for one of ordinary skill in the art at the time of invention to include costs as taught by KARWETA in the system executing the method of ZHU with the motivation to determine successful user interface design features.
Claim 14 (Original)
The combination of ZHU, KHANDEKAR, KARWETA, and FIRMAN discloses the non-transitory computer-readable medium of claim 13.
ZHU does not specifically disclose, but FIRMAN discloses, the operations further including: identifying, from a data store, physical constraints associated with the target computing components of the particular computing product (see col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability); and
iteratively generating, based on the physical constraints associated with the target computing components, a plurality of layouts of a targeted computing product (see col 2, ln 3-5; the application marketplace may be able to monitor various application metrics, such as user rankings and installation statistics, across the different versions of the application. See again col 7, ln 57-col 8, ln 10; the device groups may be defined using any appropriate number of dimensions, which may generally correspond to the capabilities or attributes of a device or device type. The various dimensions may be stored and/or updated in dimension information database 126. For example, in some implementations, the publisher system 104 may allow software publishers to define device groups using three dimensions, including an installed device framework, a display screen size, and a graphics processing capability. Determine which version match the user’s device. See col 7, ln 19-31; ranking separate versions of an application on computing devices).
ZHU discloses facilitating product research and development that includes providing a table of top product attribute clusters to meet needs based on trends and determine areas of improvement. FIRMAN discloses ranking versions of applications on computing devices to determine a best version for a computing device. It would have been obvious to determine a best version of an application for a computing device as taught by FIRMAN in the system executing the method of ZHU with the motivation to meet needs of a user based on the user’s device.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at 571-270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624