DETAILED ACTION
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 .
This communication is in response to the Amendment filed 12/1/2025.
Response to Arguments
Claims 1 – 20 are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 20 remain rejected. The rejection under 35 U.S.C. §101 to claims 12 – 17 are withdrawn in view of the amendment.
Applicant's arguments filed with respect to claims 1 – 20 have been fully considered but they are moot in view of new rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Byron et al. (US 20170220953 A1) (‘Byron’ herein after) further in view of Harper et al. (US 20210004540 A1) (‘Harper’ herein after) further in view of Brake et al. (US 20190279104 A1) (‘Brake’ herein after).
With respect to claim 1, 12, 18,
Byron discloses a computer-implemented method comprising: storing a units of measure (UoMs) and relationships of the UoMs to textual representations of the UoMs (figures 1 – 3, paragraphs 20 – 25 teach numerical expressions and the units of measure associated with them, Byron); extracting, text corresponding to a measurement of a product to determine a value of the measurement and an initial textual representation of a corresponding unit of measure (UoM) of the measurement (figures 1 – 3, paragraphs 20 – 25 teach extracting numerical expressions and the units of measure associated with the numerical expression and the product, Byron); determining, a recommended textual representation of the measurement of the product based on the initial textual representation of the UoM, the recommended textual representation comprising a corresponding textual representation stored and causing display of the recommended textual representation of the measurement of the product (figures 1 – 3, paragraphs 24 – 37 teach the feature vector from the expression program, Byron).
Byron teaches storing the plurality of units of measure but does not teach it explicitly as claimed as a knowledge graph.
However, Harper teaches storing the information and using a knowledge graph in paragraphs 38 – 40 and 44 teaching storing and presenting the data aggregates of measurements in the form of a knowledge graph. Furthermore, paragraphs 50 – 58 of Harper teach the extraction of logic and context and using algorithms and models to organize the knowledge about the subject matter.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because both references are directed to the same field of study, namely extracting and providing information. Furthermore, Harper states in paragraphs 13 – 14, by extracting context from measured values contained in a variety of journal articles or other scientific literature, a database may be created aggregating measured values from a large corpus of scientific literature and to detect numerical values or quantities contained therein. The system may then extract a portion of text (e.g., a sentence, a paragraph, or the like) surrounding each numerical value detected. The extracted text may then be encoded or mapped to a vector representation and the vector representation of the text may be analyzed using multi-turn question answering to learn the context of each numerical value.
The combination of Byron and Harper teach storing units of measure and extracting the measurement but does not specifically describe as claimed UoM synonymous with representation based on relationships.
However, Brake teaches UoM synonymous with representation based on relationships in figure 2, paragraphs 22, 27 – 29 and 31, Brake teaches in accordance with the synonym measurement module that converts the measurement value of 335 pounds to a default measurement unit and its corresponding value. The hypothesis generation module produces passages/candidate answers that include by searching the system's available knowledge sources. The hypothesis scoring module scores the passages using the synonym measurement module to compare the values found in passages to the value and provide a score reflecting the distance between the values after conversion to the same units. Brake also teaches a unit conversion in a synonym-sensitive framework for answering questions that include a measurement value. In an embodiment, a synonym measurement module may be incorporated within the QA system to identify questions that include a measurement value. The synonym measurement module may include instructions for converting a measurement unit or value found in question or a passage to a default measurement unit to enable comparison of the values. The default measurement unit may be the measurement unit specified in the question or may be a predetermined or user-specified measurement unit.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brake to the combination of Byron and Harper’s method because they are directed to the same field of study of extracting and providing information. Brake teaches the improvement in a question answer system by applying synonyms for units of measurements and their values in questions received by the question answer system in determining the most probable answers to the question by augmenting it with various equivalents.
With respect to claim 2,
Byron as modified discloses the computer-implemented method of claim 1, wherein the relationships of each of the plurality of UoMs comprise at least one of quantitative measure, dimensions, type of measurement system, symbols defined by the type of measurement system, commonly occurring representations or conversion factors for other measurement systems (paragraphs 20, 25, 37, 41, Byron).
With respect to claim 3, 15,
Byron as modified discloses the computer-implemented method of claim 1, wherein extracting, the text corresponding to the measurement of the product further comprises: detecting, by named entity recognition, the text corresponding to the measurement of the product and classifying, by a classifier, the text corresponding to the measurement of the product as the corresponding UoM from the UoMs to determine the initial textual representation (paragraphs 20, 37, 41 – 45, Byron).
With respect to claim 4,
Byron as modified discloses the computer-implemented method of claim 3, wherein the classifier is trained based on the relationships of the UoMs and a plurality of product descriptions and wherein the classifier enriches the knowledge graph based on the plurality of product descriptions (paragraphs 28, 36 – 38, 41 – 44, Byron).
With respect to claim 5,
Byron as modified discloses the computer-implemented method of claim 3, wherein the classifier is trained to disambiguate between the initial textual representation of the corresponding UoM and a different one of the UoMs with a similar symbol based on a product description (figures 3 – 5, paragraph 28 – 33 and 44 – 55, Harper).
With respect to claim 6,
Byron as modified discloses the computer-implemented method of claim 3, wherein the classifier is trained to determine the initial textual representation of the corresponding UoM when the text contains an erroneous representation of the corresponding UoM (paragraphs 28, 36 – 38, 41 – 44, Byron).
With respect to claim 7, 16, 17,
Byron as modified discloses the computer-implemented method of claim 1, wherein determining, the recommended textual representation of the measurement of the product further comprises: determining the recommended textual representation of the measurement of the product based on at least one of a location of a customer, a location of a business, a product description in a product catalog of the business, customer data of the customer, or business data of the business and converting the value of the measurement to a synonymous representation of the value based on the recommended textual representation of the measurement of the product (figures 3 – 5, paragraph 28 – 33 and 44 – 55, Harper).
With respect to claim 8,
Byron as modified discloses the computer-implemented method of claim 1, is trained based on at least one of product catalogs from different business models, product catalogs from different industry verticals, product catalogs from different locations, the knowledge graph, or search query data (figures 1, 2, paragraphs 38 – 41, Byron).
With respect to claim 9, 13, 19,
Byron as modified discloses the computer-implemented method of claim 1, further comprising: receiving a product description of the product, wherein the text corresponding to the measurement of the product is missing a textual representation of a the corresponding UoM; classifying, by a classifier, the text corresponding to the measurement of the product as the corresponding UoM from the UoMs based on the product description of the product to determine the initial textual representation and determining, the recommended textual representation of the measurement of the product further based on the product description of the product (paragraphs 28, 36 – 38, 41 – 44, Byron).
With respect to claim 10,
Byron as modified discloses the computer-implemented method of claim 1, further comprising: receiving a product description of the product in a plurality of product descriptions of a plurality of products, wherein the text corresponding to the measurement of the product comprises a textual representation of a the corresponding UoM that is different from a different textual representation of the corresponding UoM in a different one of the plurality of product descriptions ; classifying, by a classifier, the text corresponding to the measurement of the product as the corresponding UoM from the UoMs based on the plurality of product descriptions of the product to determine the initial textual representation and determining, the recommended textual representation of the measurement of the product further based on the plurality of product descriptions of the product (figures 3 – 5, paragraph 28 – 33 and 44 – 55, Harper).
With respect to claim 11,
Byron as modified discloses the computer-implemented method of claim 1, further comprising: receiving a search query for the product with the text corresponding to the measurement of the product and determining the recommended textual representation of the measurement of the product further based on the search query for the product to normalize the measurement in the search query with respect to a search engine index (figures 1 – 3, paragraphs 28, 36 – 38, 41 – 44, Byron and paragraphs 22, 27 – 29 and 31, Brake).
With respect to claim 14,
Byron as modified discloses the one or more non-transitory computer readable media of claim 12, the method further comprising normalizing textual representations of measurements of products in product descriptions of the products to normalize the measurements in the product descriptions with respect to the search engine index (figures 3 – 5, paragraph 28 – 33 and 44 – 55, Harper).
With respect to claim 20,
Byron as modified discloses the system of claim 18, wherein the instructions that when executed by the processor, cause the processor to perform operations further including: based on identifying zero results for the product in response to a number of search queries: generating taxonomies for one or more sets of products of a plurality of products based on a normalizing measurements of the plurality of products and classifying the one or more sets of products in a particular taxonomy based on the measurements and causing display of the taxonomies for the one or more sets of products of the plurality of products (figures 3 – 5, paragraph 28 – 33 and 44 – 55, Harper and paragraphs 22, 27 – 29 and 31, Brake).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170270549 A1 teaches normalizing a unit of measures of a product provided by multiple retailers.
US 20250036604 A1 teaches the model to infer a correct size value and size UOM of the product. The online system evaluates the accuracy of the inferred size value and size UOM of the product. Responsive to determining that the inferred data is accurate, the online system updates the product catalog with the corrected product attribute information.
US 20190130289 A1 teaches capturing expression as written text data, obtaining a knowledge graph representing concepts and relationships between the concepts automatically topic modeling the written text data to ascertain thought units and identify respective concepts of the thought unit, mapping a thought unit to the knowledge graph, determining that the thought unit is an original idea based on a graph distance in the knowledge graph between correlated concepts represented in the knowledge graph, and based on determining that the thought unit is an original idea, storing a representation of the original idea to an idea repository and invoking processing.
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 1/9/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166