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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 102 rejection of claims 1, 24 and 25 have been considered and found persuasive, and the rejection has been withdrawn. See detailed reason for allowance below.
Applicant's arguments with respect to 35 U.S.C. 101 rejection of claim 25 has been considered and found persuasive due to amendments, and the rejection has been withdrawn.
Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea in regard to claims 1-25 have been considered, however are not found to be persuasive due to the following reasons. Examiner’s respectfully disagrees with Applicant’s arguments because the amended claims 1, 24 and 25 remain directed to the collection and analysis of information to generate an explanation of a recommendation. The claims obtain text about a recommended item and the recommendation context, organizes that information, and uses a language model to generate text explaining why the item was recommended. Although the Applicant argues that the invention provides “post hoc” explanations for a “black box” recommendation system without accessing its internal components or training details, those features are not actually required by claims 1, 24 and 25. The claims do not require the recommendation system to be a black-box system, do not require the explanation to be generated independently from the recommendation system, and do not prohibit access to the recommender’s internal components or training information. Therefore, these asserted benefits from the Specification are not provided with the actual scope of amended claims 1, 24 and 25.
Applicant’s reliance on the newly added limitation requiring “a concatenation of data comprising a prompt sequence, the recommendation context and the item context text” is also not persuasive. This limitation merely specifies how information is arranged before being processed by the LM neural network. It does not recite a particular improvement to the architecture or operation of the LM, an improved recommendation algorithm, or a technical change to the functioning of the computer itself. The LM is still being used for its ordinary purpose of processing textual information and generated natural language text. Likewise, Applicant’s asserted benefits of generating personalized explanations, highlighting relevant information, increasing transparency, and improving user trust concern primarily the content and usefulness of the information presented to the user, rather than an improvement computer technology.
Finally, considering amended claim 1, 24 and 25 as a whole, the additional computer and LM limitations do not integrate the abstract idea into a practical technological application or provide significantly more than the abstract idea. The claimed computers obtain information, concatenate selected information into an input sequence, process that sequence using a LM, and output explanatory information. The claims do not specify a new neural network architecture, a new model training technique, improved computer resource utilization, or another particular technological mechanism that improves the operation of the computer or recommendation system. Accordingly, merely using LM and a particular organization of input information to automate the generation of recommendation explanations does not transform the underlying abstract information processing activity into patent eligible subject matter. Therefore, the claims rejected under 101 Abstract Idea are maintained.
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 1-4 and 6-25 are rejected under 35 U.S.C. 101.
Claim 1 and 24-25 are directed to the abstract idea of explaining a recommendation using information analysis and natural language generation, which falls within judicially recognized abstract ideas such as collecting information, analyzing it, and presenting the results (e.g., mental processes and methods of organizing human activity). The claim merely obtains contextual information about a recommended item and the recommendation context, combines that information, and generates an explanation describing why the recommendation was made. Explaining recommendations to users is a fundamentally informational and cognitive task that mirrors how a human would justify a recommendation using contextual reasoning, even if automated.
The claim does not integrate this abstract idea into a practical application or technical improvement. The use of a language model neural network is recited at a high level of generality and is used only as a generic tool to perform the abstract task of explanation generation. The claim does not improve the functioning of the computer itself, improve a specific machine learning architecture, reduce computational cost, or solve a technological problem in computer systems.
The claim elements amount to no more than well-understood, routine, and conventional computer activities: obtaining text data, forming an input sequence, processing that input with a known machine learning model, and outputting explanatory text. The claim does not recite any specialized data structures, novel training techniques, model architectures, or constrained deployment that would transform the abstract idea into patent-eligible subject matter.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (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 no further improvement to the computing device.
Dependent claims 2-4 and 6-23 are further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in user/customer review system.
Claim 2: merely presents generated information to a user, which is abstract data presentation.
Claim 3: adds receiving a user request and responding with information, which is routine interaction and abstract information exchange.
Claim 4: identifies topics and provides links to related content, which is organizing and presenting information.
Claim 6: uses example input-output pairs as guidance, which is abstract instructional information.
Claim 7: recites generic machine learning training concepts without a technical improvement.
Claim 8: uses a chain-of-thought prompt, which is an abstract reasoning aid.
Claim 9: adds user interest information to content analysis, which is abstract personalization.
Claim 10: generates descriptive text from metadata, which is abstract information summarization.
Claim 11: applies the abstract process to video metadata, a field-of-use limitation.
Claim 12: applies the abstract process to news article metadata, a field-of-use limitation.
Claim 13: applies the abstract process to product metadata, a field-of-use limitation.
Claim 14: applies the abstract process to electronic book metadata, a field-of-use limitation.
Claim 15: applies the abstract process to music metadata, a field-of-use limitation.
Claim 16: applies the abstract process to software application metadata, a field-of-use limitation.
Claim 17: limits description generation to summaries, which is abstract information condensation.
Claim 18: summarizes contextual information, which is abstract data analysis and presentation.
Claim 19: summarizes conversational turns, which is abstract conversational analysis.
Claim 20: summarizes search query text, which is abstract information processing.
Claim 21: uses prior search queries as context, which is abstract historical data analysis.
Claim 22: uses prior recommended items as context, which is abstract recommendation history analysis.
Claim 23: uses user interaction data as context, which is abstract behavioral data analysis.
Allowable Subject Matter
Claims 1-4 and 6-25 would be allowable if the Applicant can overcome the 101 Abstract Idea rejection set forth.
The following is a statement of reasons for the indication of allowable subject matter: Ni et al. (“Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects”; Nov. 2019) in view of Li et al. (“Personalized Prompt Learning for Explainable Recommendation”; Mar. 2023):
Ni teaches generating personalized natural language explanations for recommendations using textual information associated with both a user and an item. In particular, Ni builds a user/item “justification reference” containing “justifications that the user has written (or justifications about the item)” and obtains corresponding user personas and item profiles based on fine grained aspects, thereby providing user/recommendation context and item context. Ni then uses a Ref2Seq neural model having “two sequence encoders that learn user and item latent representations by taking previous justifications as references” and a sequence decoder that incorporates those representations to “generate personalized justifications.” Ni describes the resulting output as justification text that “would explain why item i fits user u’s interests.” (see [3.1-3.2] [pgs. 190-191] [Fig. 1])
Li teaches using prompt learning with a pre-trained LM to generate natural language explanations explaining why an item is recommended to a user. Li explains that the goal is to generate a natural language sentence for a user item pair “to justify why i is recommended to u” and teaches using information related to “both the target user and target item” because it represents the user’s preferences and the item’s attributes. (see [1.] [3.]) Li further teaches constructing an explanation-generation prompt containing user and item inputs, Table 2 illustrates “X1 X2 (explain the recommendation:) Y” – and describes an “input sequence to the pre-trained model” containing the prompt information. (see [3.1] [3.2] [Table 2] [Fig. 3])
The difference between the prior art and the claimed invention is that Ni nor Li explicitly teach wherein the input sequence comprises a concatenation of data comprising a prompt sequence, the recommendation context and the item context text.
Therefore, it would not have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Ni and Li to include wherein the input sequence comprises a concatenation of data comprising a prompt sequence, the recommendation context and the item context text.
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 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.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
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