DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 01/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites
“1. A system, comprising:
a processor; and
a non-transitory memory, storing instructions, that when executed, cause the processor to:
receive an item data structure including textual information;
determine a first context associated with the textual information;
generate a plurality of keywords using a first trained model that receives the textual information and the first context;
receive a plurality of matching item data structures including respective textual information corresponding to a plurality of matching items associated with the item data structure;
determine one or more second contexts associated with the respective textual information;
generate a set of reference keywords using a second trained model that receives the respective textual information and one of the one or more second contexts;
determine whether the set of reference keywords includes at least one keyword in the plurality of keywords;
in accordance with a determination that the set of reference keywords includes the at least one keyword in the plurality of keywords:
determine a relevancy score between the at least one keyword in the plurality of keywords and the first context using the first trained model; and
in accordance with a determination that the relevancy score exceeds a first threshold, generate an interface that includes the at least one keyword in the plurality of keywords.” as recited in Claim 1.
The independent Claims 1, 8 and 15 recite substantially the same concept but do so in the context of a system, a method and a non-transitory computer readable medium.
The limitations recited in the independent claims as drafted covers a mental process. The underlying abstract idea revolved around what happen once a human summarize a description for a newly listed item. More specifically, the human reads the description of the newly listed item, determines a first context associated with the description of the newly listed item, writes down a plurality of keywords based on the description and the first context, receives a plurality of descriptions of other items including respective textual information which matching with the newly listed item, determines a second context associated the plurality of description of other items, writes down a set of reference keyword based on the second context and the respective textual information, determines that whether the set of reference keywords includes at least one keyword in the plurality of keywords, if so, determines whether a relevance score between the at least one keyword in the plurality of keywords and the first context exceed a threshold, if so, write down that at least one keyword on the paper.
The judicial exception is not integrated into a practical application. In particular, claims recite the additional limitations of “a processor”, “a non-transitory memory”, and “a non-transitory computer readable medium”. The additional element(s) or combination of elements such as a processor, a non-transitory memory and a non-transitory computer readable medium in the claim(s) other than the abstract idea per se amount(s) to no more than (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device other than summarize the description of the newly listed item based on the related keyword(s) in the similar item. The mere recitation of a processor, a non-transitory memory and a non-transitory computer readable medium and/or the like is akin of adding the word “apply it” and/or “use it” with a computer in conjunction with the abstract idea. The paragraph [0035] of the specification discloses “[0035] The keyword and interface generation computing device 4 is further operable to communicate with the database 14 over the communication network 23. For example, the keyword and interface generation computing device 4 may store data to, and read data from, the database 14. The database 14 may be a remote storage device, such as a cloud-based server, a disk (e.g., a hard disk), a memory device on another application server, a networked computer, or any other suitable remote storage. Although shown remote to the keyword and interface generation computing device 4, in some embodiments, the database 14 may be a local storage device, such as a hard drive, a non-volatile memory, or a USB stick. The keyword and interface generation computing device 4 may store interaction data received from the web server 6 in the database 14. The keyword and interface generation computing device 4 may also receive from the web server 6 user session data identifying events associated with browsing sessions, and may store the user session data in the database 14.”
As filed in the specification, the computer is listed as a general-purpose computer and are mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claim(s) recite “a first trained model” and “a second trained model” at high level of generality. Rather, these limitations only recite the outcome of “generate a plurality of keywords”, “generate a set of reference keywords”, “determine a relevance score” and do not include any technical details how the “generate” and “determine” are accomplished. See MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims further do not remedy the issues noted above. More specifically, Claims 2, 9 and 16 recite mental processes of selecting, sorting and removing keywords. Claims 3, 10 and 17 recite a mental process of ranking words based on the relevance score. Claims 4, 11 and 18 recites a mental process of identifying one or more words absent from the textual information. Claims recite a prompt and the trained model. The human could user a prompt (e.g., instruction) to identify one or more words absent from the textual information. Please, see the trained model analysis in the independent claims. Claims 5, 12 and 19 recite the similar features as Claims 4, 11 and 18 respectively. Claims 6, 13 and 20 merely define the set of reference keywords. Claims 7 and 14 recite mental process of determining the at least one keywords is an allowed keyword.
For at least the supra provided reasons, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
5. Claims 1-20 are allowed in view of the prior art of record. However, claims 1-20 are rejected under 101 abstract idea, and for the application to pass to allowance these rejections need to be overcome. Any amendments to overcome the 101 rejection that results in any change in scope require further search and/or consideration in order to determine it allowability.
The following is a statement of reasons for the indication of allowable subject matter: the prior art(s) taken alone or in combination fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“receive a plurality of matching item data structures including respective textual information corresponding to a plurality of matching items associated with the item data structure;
determine one or more second contexts associated with the respective textual information;
generate a set of reference keywords using a second trained model that receives the respective textual information and one of the one or more second contexts;
determine whether the set of reference keywords includes at least one keyword in the plurality of keywords;
in accordance with a determination that the set of reference keywords includes the at least one keyword in the plurality of keywords:
determine a relevancy score between the at least one keyword in the plurality of keywords and the first context using the first trained model; and
in accordance with a determination that the relevancy score exceeds a first threshold, generate an interface that includes the at least one keyword in the plurality of keywords.” as recited in Claim 1.
Claims 8 and 15 recites the similar features as Claim 1.
The closest prior arts found as following.
a. Shan et al. (US 2022/0172269 A1.) In this reference, Shan et al. discloses technique for providing product description (Shan et al. [0088] The pseudo-tag filtering module 330 is configured to, upon receiving the ranked pseudo-tags and their confidence scores from the pseudo-tag scoring and ranking module 328, filter the pseudo-tags to obtain filtered tags, and add the filtered tags to the current true tags of the product. In certain embodiments, the filtering is performed by comparing the confidence scores of the pseudo-tags with predefined threshold. When the confidence score of a pseudo-tag equals to or is greater than the threshold, the pseudo-tag is regarded as a true tag. In certain embodiments, the predefined threshold for different types of tags have different values. For example, the threshold for the brand tags may be 2 or 3, while the threshold for the human feelings may be in the range of 5-20, for example 10. In certain embodiments, threshold of the same types of tags for different types of products may be different. In certain embodiments, the threshold for the same type of tag may vary in different iteration of the lifelong learning, where the threshold value may increase in a later iteration when there are already a lot of available tags for the products.) Shan et al. predicts pseudo tags from the product descriptions, calculates confidence scores of the pseudo tags, compares the confidence scores with a threshold, and defines the pseudo tags as true tags when the confidence scores are greater than the threshold, adds the true tags to the seed tags characterizing product. Shan et al. provides the updated tags as keywords of the corresponding product, and the user use the keywords to find the corresponding products via a search engine (Shan et al. see paragraph [0025 and 0026]). Shan et al. does not teach and/or suggest determining a first context, generating a plurality of keywords, determining a second context, generating a set of reference keywords, determining a relevance score between at least one keyword in the set of reference keywords and the first context, and generate an interface that includes the at least one keyword as recited in claims. Thus, Shan et al. fails to teach and/or suggest the allowable subject matter.
b. Osanai et al. (US 2026/0051097 A1). In this reference, Osanai et al. discloses technique for generating a product description (Osanai et al. [0276] In addition, in the above-described example, a product description is generated based on the input product name (the product name displayed in the product name region (a)) and keywords automatically generated based on the product name (keywords output in the keyword region (b)). However, there is no limitation to this mode, and the product description may be generated based on the input product name and input keywords (in a non-limiting example, keywords that are displayed in the keyword region (b) due to the user inputting the keywords in this region), [0277] In addition, in the above-described example, based on the user inputting a product name in the product name region (a), keywords may then be automatically generated based on the product name and output to the keyword region (b) without the need for user operation, a predetermined number of product images may be automatically generated based on the product name, keywords, and the like and output to the image generation result region (e), a product description may be automatically generated based on the product name, keywords, and the like and output to the product description region (c), and a predetermined number of advertisements may be automatically generated based on the product name, product description, and any product image (in a non-limiting example, an automatically selected product image) and output to the advertisement generation result region (g).) Osanai et al. discloses generating a product description including the product name and the keywords. The keyword is included in the content of the of the product. However, Osanai et al. does not teach and/or suggest determining a first context, generating a plurality of keywords, determining a second context, generating a set of reference keywords, determining a relevance score between at least one keyword in the set of reference keywords and the first context, and generate an interface that includes the at least one keyword as recited in claims. Thus, Osanai et al. fails to teach and/or suggest the allowable subject matter.
c. Unnikrishnan et al. (US 2025/0200635 A1.) In this reference, Unnikrishnan et al. discloses techniques for generating a summarization of the description (Unnikrishnan et al. [0005] A method, apparatus, and non-transitory computer readable medium for neural compositing are described. One or more aspects of the method, apparatus, and non-transitory computer readable medium include receiving a query relating to an item and a summarization type indicating an emphasis on item similarities or item differences, obtaining, using a search component, descriptions of items relevant to the query, generating input data for a machine learning model based on the descriptions and the summarization type, and generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or item differences based on the summarization type, [0047] In various embodiments, a summarizer 200 receives a user query requesting information on an item, where the query can be a natural language query. The large language model 260, summarization component 240, ideation component 270, and search component 250 is stored in computer memory 220 of the summarizer 200. The summarizer 200 includes a summarization component and an ideation component that provides users with the ability to generate comparisons and fine tune the way products are compared and summarized to provide different insights that would be useful to the user. The ideation is driven by the trained LLM. The summarizer 200 can be used for online searches, and used specifically for product searches and sale of goods. The summarizer 200 can be utilized for enterprise-to-enterprise engagement.) Unnikrishnan et al. receives a query relating to the an item and a summarization type indicating an emphasis on item similarities or item differences, obtaining, using a search component, descriptions of items relevant to the query, generating input data for a machine learning model based on the descriptions and the summarization type, and generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or item differences based on the summarization type. However, Unnikrishnan et al. does not teach and/or suggest determining a first context, generating a plurality of keywords, determining a second context, generating a set of reference keywords, determining a relevance score between at least one keyword in the set of reference keywords and the first context, and generate an interface that includes the at least one keyword as recited in claims. Thus, Unnikrishnan et al. fails to teach and/or suggest the allowable subject matter.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Maschmeyer et al. (US 2024/0320444 A1.) In this reference, Maschmeyer et al. disclose a method and a system for automatically generating a product description based on the user-inputted product features and keywords.
b. Norel et al. (US 2025/0218056 A1.) In this reference, Norel et al. disclose a method and a system for generating picture description task images.
c. Qiang et al. (US 2025/0173518 A1.) In this reference, Qiang et al. disclose a method and a system for generating a description for the item listing based on information in an entry for item in the catalogs.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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/THUYKHANH LE/Primary Examiner, Art Unit 2655
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