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 .
Claims Status
Claims 1, 14 and 18 have been amended.
Claim 13 has been canceled.
Claims 1-12 and 14-20 are currently pending and rejected.
Response to Arguments
35 USC 101 rejection
Applicant's arguments with respect to the rejection of claims 1-20 under 35 USC 101, as being directed to a judicial exception, have been fully considered but are not persuasive, in view of the accompanying amendments and in view of MPEP 2106. The rejection under 35 USC 101 is explained in further detail below.
Examiner appreciates Applicant’s inclusion of additional limitations, however, the Examiner asserts that even with the inclusion of additional features, the claims remain abstract and directed to a judicial exception. As written, the claims merely set forth a process for providing product information, which is considered to be an abstract idea as it relates to ‘certain methods of organizing human activity,’ namely marketing or sales activities or behaviors.
Although the claim may use technology (i.e. a computer) to perform steps related to solving a problem, the claim does not amount to a ‘technical improvement’ as the technology (i.e. a computer or other machinery) is merely used in its ordinary capacity for economic or other tasks (e.g., to receive and transmit data). Furthermore, the claims merely disclose usage of a large language model in a new environment. This new environment is providing information on a variety of products. The claimed methods are not rendered patent eligible by the fact that (using existing technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.
Examiner reiterates that although claims 1 and 18 may use technology (i.e. a computer, etc.) to perform steps related to solving a problem, the claim does not amount to a ‘technical improvement’ as the technology (i.e. a computer or other machinery) is merely used in its ordinary capacity for economic or other tasks (e.g., to store, receive and transmit data). Further clarification with regards to the specific structures performing specific functions and the improvements achieved by the large language model may assist with the analysis of these claims. “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology” (See MPEP 2106.05(A)).
The additional elements are merely recited at a high level of generality and amount to little more than the mere instructions to implement an abstract idea on a computer or similar hardware. Further, these elements represent little more than a general link to a technological environment (i.e. a mere attempt to restrict use of the idea to a technical environment such as the Internet or computer networks – see Ultramercial, Inc. v. Hulu, LLC) as currently written. In each case, the courts have found such limitations insufficient to qualify as “significantly more” when recited in a claim with a judicial exception (see MPEP 2106.05(A)).
As written, the claims fail to be significantly more than the abstract idea because the claims use a computer or other machinery in its ordinary capacity for economic or other tasks or simply add a general purpose computer or computer components (i.e. machine learning model) after the fact to an abstract idea. Therefore, the claims continue to be viewed as examples of an abstract idea without significantly more and thus lack subject matter eligibility.
Additionally, where certain dependent claims rely upon similar additional elements as recited in claims 1 and 18 these do not result in significantly more than the abstract idea itself. The additional elements of the dependent claims are treated at least similarly as those discussed above with respect to claims 1 and 18.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B of the Mayo framework at least similar rationale as discussed above regarding claims 1 and 18.
In view of the above, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. For at least these reasons above, the rejections under 35 USC 101 have been maintained and are explained in further detail below.
35 USC 102 rejection
Applicant’s arguments with respect to the previous rejection of claims 1-20 under 35 USC 102 have been fully considered but are not persuasive, in view of the accompanying amendments.
Applicant's amended claim 1 now requires “identifying, by the LLM, a plurality of differentiators by mapping the user query to a product category based on a semantic relationship between the user query and a query cluster database”. These limitations had not been previously been recited and change the scope of the invention. Applicant argues that the cited prior art does not anticipate or teach the additional features a plurality of differentiators by mapping the user query to a product category based on a semantic relationship between the user query and a query cluster database. Examiner disagrees with Applicant’s assessment. The Sacheti reference explicitly teaches “the dialog history can include context obtained by the search engine 110 during conversations; for instance, with respect to a conversation, the dialog history 118 can include content generated based upon queries set forth by the user (and/or other users) and/or the GLM 112 during the conversation, content from webpages identified by the search engine 110 based upon queries set forth by the user and/or the GLM 112 during the conversation, and so forth” (para [0025]). Thus, Sacheti allows for the comparison of the present user query to previous queries by the same or other users to determine context of the search. Here, the context determined is the same as that which applicant has identified as a semantic relationship between the user query and the query cluster database. Further, Sacheti goes on to disclose “when a user initiates a search with a query that pertains to a comparison between entities, and then continues to interact with the GLM in a chat to specify important attributes of the entities for which the user is searching, such attributes can be associated with the given product as being important attributes of the product. For a given product, attributes that are more frequently queried can be assigned greater weight or otherwise be more highly ranked then less frequently queried attributes” (para [0026]). Therefore, Sacheti utilizes the determined context information for the search to identify a weighted hierarchy of attributes (plurality of differentiators) for a particular product comparison based on attributes more frequently queried (product category).
The rejection under 35 USC 102 has been maintained and is explained in further detail below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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.
Step 1:
Claims 1-17 are directed to a method, which is a process. Claims 18-20 are directed to a system, which is a machine. Therefore, claims 1-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Representative claim 1 sets forth the following limitations which recite the abstract idea of providing product information:
obtaining input data comprising a user query and query context data;
processing the user query and the query context data to generate a set of search results;
identifying a plurality of differentiators by mapping the user query to a product category based on a semantic relationship between the user query and a query cluster;
defining a schema associated with a plurality of differentiators based on the user query, the query context data, and the set of search results;
extracting information associated with the plurality of differentiators from the set of search results;
generating a comparative data structure using the schema, and the information associated with the plurality of differentiators; and
comparing the set of search results based on the information associated with the plurality of differentiators using the comparative data structure.
The recited limitations above set forth steps to provide product information. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner notes that representative claim 1 fails to recite any additional elements such as a computer, a large language model, etc. However, even if claim were to recite additional features such as these, the claims would fail to integrate the recited judicial exception into a practical application of the exception. The claims would merely include instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements would do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the lack of additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, even including an additional element of a processor or computer to perform the steps would amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even if considered as an ordered combination, any additional elements of claim 1 would not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-12 and 14-17 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for providing product information. Thus, each of claims 2-12 and 14-17 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-12 and 14-17 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claim 18 (system), the claim recites substantially similar limitations as set forth in claim 1. As such, claim 18 and its dependent claims 19-20 are rejected for at least similar rationale as discussed above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sacheti et al. (U.S. Pre-Grant Publication No. 2024/0354309 A1) (“Sacheti”).
Regarding claims 1 and 18, Sacheti teaches a computer-implemented method (and related system), comprising:
obtaining, by a computing system, input data comprising a user query and query context data (Fig. 2; para [0032], query submitted to the client computing device 102 by a user thereof is obtained by the query classifier 126. In an example, the query is “new tennis racket”);
processing, by a large language model (LLM) operating on the computing system, the user query and the query context data to generate a set of search results (Fig. 2; para [0033], query classifier 126 provides the query and prompt information assigned to the intent(s) to the GLM 112. The prompt information may include an instruction to the GLM 112 that the GLM 112 is to generate a table and populate the table with attributes of entities identified by the search system);
identifying, by the LLM, a plurality of differentiators by mapping the user query to a product category based on a semantic relationship between the user query and a query cluster database (para [0025]-[0026]);
defining, by the LLM, a schema associated with a plurality of differentiators based on the user query, the query context data, and the set of search results (Fig. 2; para [0035], search system 110 provides at least some information from the search results to the GLM 112. Such information can include portions of webpages identified by the search system 110 as being relevant to a query in the queries, entity information from a knowledge graph, product information from a product catalog, and so forth.);
extracting, by the LLM, information associated with the plurality of differentiators from the set of search results (Fig. 2; para [0035]; para [0039], search system 110 searches the data store(s) 114-118 based upon the queries received from the GLM 112, and at 224 the search system 110 obtains search results based upon the searches. At 226, the search system 110 provides information extracted from the search results to the GLM 112);
generating, by the LLM, a comparative data structure using the schema, and the information associated with the plurality of differentiators (Fig. 2; para [0036], GLM 112 generates a table that includes identities of multiple tennis rackets and values of attributes associated with such tennis rackets (where example attributes are “brand”, “weight”, “length”, “material”, “flex”, “price”, and so forth)); and
comparing, by the LLM, the set of search results based on the information associated with the plurality of differentiators using the comparative data structure (para [0042], prompt provided to the GLM 112 by the query classifier 126 and/or the search system 110 can construct the GLM 112 to generate contents of table cells in natural language; example contents can include “pros” and “cons” of a product, a summary of user reviews of a product, amongst other information.).
Regarding claim 2, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein extracting the information associated with the plurality of differentiators further comprises ranking, by the LLM, each search result within the set of search results based on the information associated with the plurality of differentiators (para [0026], For a given product, attributes that are more frequently queried can be assigned greater weight or otherwise be more highly ranked then less frequently queried attributes. Attributes with higher weights can be selected by the GLM for inclusion in a product comparison table over attributes having lower weights.).
Regarding claim 3, Sacheti teaches the above method of claim 2. Sacheti also teaches wherein extracting the information associated with the plurality of differentiators further comprises:
selecting, by the LLM, a subset of search results from the set of search results based on the ranking (para [0035], obtains search results identified in the data store; para [0026]); and
extracting, by the LLM, information associated with the plurality of differentiators from the subset of search results (para [0035], where the search results can include webpages identified as being relevant to the query, supplemental content (e.g., product descriptions in a product catalog), and/or other information; para [0026]).
Regarding claim 4, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein generating the comparative data structure comprises:
initializing, by the LLM, the comparative data structure (Fig. 2; para [0033], prompt information may include an instruction to the GLM 112 that the GLM 112 is to generate a table); and
populating, by the LLM, the comparative data structure with the information associated with the plurality of differentiators (Fig. 2; para [0033], prompt information may include an instruction to the GLM 112 that the GLM 112 is to … populate the table with attributes of entities identified by the search system).
Regarding claim 5, Sacheti teaches the above method of claim 4. Sacheti also teaches wherein generating the comparative data structure comprises grounding, by the computing system, the information associated with the plurality of differentiators using a grounding process (para [0030], generate a table and populate the table with values of attributes of entities that are relevant to the query, images that are relevant to the query, and so forth).
Regarding claim 6, Sacheti teaches the above method of claim 4. Sacheti also teaches wherein generating the comparative data structure comprises normalizing, by the computing system, the information associated with the plurality of differentiators based on the schema (para [0030], generate a table and populate the table with values of attributes of entities that are relevant to the query, images that are relevant to the query, and so forth).
Regarding claim 7, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein generating the comparative data structure comprises:
extracting, by the computing system, image data associated with the set of search results (para [0035]; para [0036], GLM 112 can further optionally include images of rackets in the table, videos pertaining to the rackets in the table, and so forth); and
populating, by the computing system, the comparative data structure with the image data (para [0036], GLM 112 can further optionally include images of rackets in the table, videos pertaining to the rackets in the table, and so forth).
Regarding claim 8, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein the method further comprises generating, by the LLM, a comparative report based on the information associated with each differentiator of the plurality of differentiators (para [0042], construct the GLM 112 to generate contents of table cells in natural language; example contents can include “pros” and “cons” of a product, a summary of user reviews of a product, amongst other information.).
Regarding claims 9 and 19, Sacheti teaches the above method and system of claims 1 and 18. Sacheti also teaches wherein processing the user query comprises mapping the user query to a product category based on the query context data (para [0025], dialog history can include context obtained by the search engine 110 during conversations; for instance, with respect to a conversation, the dialog history 118 can include content generated based upon queries set forth by the user (and/or other users) and/or the GLM 112 during the conversation,).
Regarding claims 10 and 20, Sacheti teaches the above method and system of claims 9 and 19. Sacheti also teaches wherein mapping the user query to the product category further comprises:
assigning, using the LLM, the user query to at least one query cluster of a plurality of query clusters based on the query context data (para [0025]); and
mapping, using the LLM, the user query to the product category based on the assignment (para [0025]).
Regarding claim 11, Sacheti teaches the above method of claim 9. Sacheti also teaches wherein the method further comprises processing, by the computing system, information associated with the product category to identify a plurality of product lines (para [0047]).
Regarding claim 12, Sacheti teaches the above method of claim 11. Sacheti also teaches wherein defining the schema associated with the plurality of differentiators further comprises defining the schema based on the plurality of product lines (para [0047]).
Regarding claim 14, Sacheti teaches the above method of claim 13. Sacheti also teaches wherein defining the schema associated with the plurality of differentiators further comprises:
ranking, by the LLM, the plurality of differentiators based on the query context data (para [0025]-[0026]; para [0047]); and
defining, by the LLM, the schema associated with the plurality of differentiators based on the ranking (para [0025]-[0026]; para [0047]).
Regarding claim 15, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein the method further comprises:
generating, by the computing system, a smart prompt based on the user query, location data associated with a user, and information associated with the plurality of differentiators (para [0022], computing system 100 is configured to obtain information based upon a query set forth by a user and is further configured to provide the obtained information as a portion of a prompt to a GLM.; para [0027]); and
identifying, by the LLM, the set of search results based on the smart prompt (para [0027]).
Regarding claim 16, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein processing the user query comprises comparing the user query to a set of query criteria (para [0030], query classifier 126 can classify a received query as one that is associated with an intent of comparing entities).
Regarding claim 17, Sacheti teaches the above method of claim 1. Sacheti also teaches wherein identifying the set of search results further comprises:
identifying, by the computing system, a first set of search results based on the user query and the query context data (para [0035], obtains search results identified in the data store; para [0026]);
extracting, by the LLM, information associated with the processed user query from the first set of search results (para [0035], where the search results can include webpages identified as being relevant to the query, supplemental content (e.g., product descriptions in a product catalog), and/or other information; para [0026]); and
generating, by the computing system, a second set of search results based on the information associated with the processed user query (para [0080], generating the prompt also includes providing the query to the generative language model, where the generative language model generates a second query based upon the query. Generating the prompt additionally includes providing the search system with the second query, where the search system identifies second search results based upon the query. Generating the prompt further comprises including a portion of the second search results in the prompt.).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
