DETAILED ACTION
This action is in response to claims filed 07 December 2023 for application 18532510 filed 07 December 2023. Currently claims 1-20 are pending.
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 .
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 an abstract idea without significantly more.
In step 1, claims 1, 9 and 16 are directed to the statutory category of a method, a system and an article of manufacture.
In step 2a prong 1, claims 1, 9 and 16 recite, in part, receiving a list of suggestions, generating content item feature vectors, generating personalized feature vectors associated with a user, generating predictions for the content items, assigning rankings, and generating suggestions by selecting content based on the rankings. The limitations of receiving, generating and assigning are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “computer-implemented”, “processor”, and “memory” in the context of the claims, the limitations encompass a human determining relations between content and user activity, ranking predictions and suggesting an answer or piece of content in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “computer-implemented”, “processor”, and “memory”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer-implemented”, “processor”, and “memory” to perform the steps of the claims 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. The claim is not patent eligible.
Claims 2-8, 10-15 and 17-20 recite further limitations of a plurality of probabilities, based on position embeddings, activity data comprises search or transaction data, personalized re-rankings, generating training data and training the model, labelling search query-content items record pairs, and re-ranking the content items. These limitations amount to the same abstract idea identified above. The only additional element is “training the model”, however, this is merely applying the additional element to the abstract idea and thus, does not amount to a practical application or significantly more.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lamba et al. (US 20220366295) in view of Chang et al. (Interactive Healthcare Robot Using Attention-Based Question-Answer Retrieval and Medical Entity Extraction Models).
Regarding claims 1, 9 and 16, Lamba discloses:
A computer-implemented method comprising:
receiving, by one or more processors, a list of suggestions that comprises a plurality of content items associated with a plurality of entities (“In one example, a user having a particular set of user attributes accesses screen 400 within the application after accessing one or more other pages (e.g., which are indicated in clickstream data). Screen 400 includes a control 402 by which the user may search for help content by entering text. Screen 400 further includes content recommendations 404, which comprise help articles that are predicted to be relevant to the user, such as using content prediction model 150 as described above with respect to FIGS. 1-4. Content recommendations 404 may be “pre-search” recommendations, as they are provided to the user before the user has searched for content, and represent content items predicted to be relevant to the user. In an example, the features of the user (e.g., user attributes and clickstream data) are provided as inputs to content recommendation model 150 of FIGS. 1-3, and content recommendations are determined based on outputs from the model. The model may have been trained based on embeddings determined using embedding model 120 of FIGS. 1 and 2, as described above.” [0059]);
generating, by the one or more processors, a plurality of content item feature vectors associated with the plurality of content items (“Features 302 of these new content items are provided as inputs to embedding model 120 (e.g., as the new content items become available), which outputs embeddings 322 of the new content items. Embeddings 322 of the new content items may be generated on an ongoing basis as the new content items become available, and may be stored for comparison with outputs of content prediction model 150. For example, at content recommendation time (e.g., when the user accesses a page, such as a help page, on which content is to be recommended), embeddings 322 of the new content items are provided to content recommender 350 for use in determining whether any new content items may also be recommended to the user based on the embeddings 320 output by content prediction model 150. Content recommender 350 may determine similarity measures between embeddings 322 and embeddings 320 to determine whether any of embeddings 322 are similar to any of embeddings 320. Any new content items with embeddings that are similar to those output by content prediction model 150 may also be recommended to the user as content recommendations 320.” [0057]);
generating, by the one or more processors, one or more personalized feature vectors based on activity data associated with a user (“In one example, a user having a particular set of user attributes accesses screen 400 within the application after accessing one or more other pages (e.g., which are indicated in clickstream data). Screen 400 includes a control 402 by which the user may search for help content by entering text. Screen 400 further includes content recommendations 404, which comprise help articles that are predicted to be relevant to the user, such as using content prediction model 150 as described above with respect to FIGS. 1-4. Content recommendations 404 may be “pre-search” recommendations, as they are provided to the user before the user has searched for content, and represent content items predicted to be relevant to the user. In an example, the features of the user (e.g., user attributes and clickstream data) are provided as inputs to content recommendation model 150 of FIGS. 1-3, and content recommendations are determined based on outputs from the model. The model may have been trained based on embeddings determined using embedding model 120 of FIGS. 1 and 2, as described above.” [0059]);
generating, by the one or more processors, a plurality of predictions for the plurality of content items based on the plurality of content item feature vectors and the one or more personalized feature vectors (“Screen 400 further includes content recommendations 404, which comprise help articles that are predicted to be relevant to the user, such as using content prediction model 150 as described above with respect to FIGS. 1-4.” [0059]);
generating, by the one or more processors, one or more suggestions, responsive to a search input received from the user, by selecting one or more of the plurality of content items (“Once trained, the machine learning model is used to predict content items that may be relevant to a user based on features of the user, and content items may be recommended to the user based on the predictions. For example, a plurality of features of the user may be provided as inputs to the machine learning model and the machine learning model may output, in response to the inputs, an indication of one or more embeddings of content items. One or more content items may then be recommended to the user determining, based on the one or more embeddings of content items (e.g., which may include recommending content items not indicated in the output that have similar embeddings to the content items indicated in the output). For example, the content items may be recommended to the user via a user interface.” [0068]).
Lamba does not explicitly disclose, however, Chang teaches:
assigning, by the one or more processors, a plurality of rankings to the plurality of content items based on the plurality of predictions (Fig 2 top-k candidates);
based on the plurality of rankings (Fig 2 top-k candidates, question answer re-rank, answer).
Lamba and Chang are in the same field of endeavor of content retrieval and are analogous. Lamba discloses an exemplary system of vectorizing users and content and suggesting likely results. Chang teaches ranking and reranking content items. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the search and suggestion system of Lamba with the known ranking and reranking as taught by Chang to yield predictable results of more accurate search/retireval results.
Regarding claim 2, Lamba discloses: The computer-implemented method of claim 1, wherein the plurality of predictions comprises a respective plurality of probabilities of the user selecting the plurality of content items (“Content recommendations 404 include articles entitled “calculating state and local tax deductions,” “itemized deductions explained,” and “limits on state and local tax deductions.” For instance, the user's features may indicate that the user is likely to be interested in information related to particular types of itemized tax deductions, as determined using machine learning techniques described herein.” [0060]).
Regarding claims 3 and 10, Lamba discloses: The computer-implemented method of claim 1 further comprising generating the plurality of predictions based on a plurality of position embeddings associated with the list of suggestions (“Once trained, the machine learning model is used to predict content items that may be relevant to a user based on features of the user, and content items may be recommended to the user based on the predictions. For example, a plurality of features of the user may be provided as inputs to the machine learning model and the machine learning model may output, in response to the inputs, an indication of one or more embeddings of content items. One or more content items may then be recommended to the user determining, based on the one or more embeddings of content items (e.g., which may include recommending content items not indicated in the output that have similar embeddings to the content items indicated in the output). For example, the content items may be recommended to the user via a user interface.” [0068]).
Regarding claims 4 and 11, Lamba discloses: The computer-implemented method of claim 1, wherein the activity data comprises at least one of search session data or transaction data (“In an example, the features of the user (e.g., user attributes and clickstream data) are provided as inputs to content recommendation model 150 of FIGS. 1-3, and content recommendations are determined based on outputs from the model.” [0059]).
Regarding claims 5, 12 and 17, Lamba discloses: The computer-implemented method of claim 1 further comprising generating the plurality of predictions by using a personalized re-ranking machine learning model comprising a transformer machine learning model (“Operations 500A begin at step 502 with determining embeddings of a plurality of content items using an embedding model. For example, features of each of the plurality of content items (e.g., derived from titles of the content items) may be provided as inputs to the embedding model, and the embedding model may output embeddings of the plurality of content items based on the inputs. In some embodiments, the embedding model is a transformer model that is trained to determine embeddings of text sequences.” [0063]).
Regarding claims 6, 13 and 18, Lamba discloses: The computer-implemented method of claim 5 further comprising:
generating training data based on the activity data; and training the personalized re-ranking machine learning model based on the training data (“Operations 500A proceed to step 510 with training the machine learning model, using the training data set, to output corresponding embeddings (and, in some embodiments, identifiers) of relevant content items for users based on features of the users. In some embodiments, training the machine learning model includes providing the features of the plurality of users in the training data set as inputs to the machine learning model, comparing outputs received from the machine learning model in response to the inputs to the respective labels in the training data set, and iteratively adjusting one or more parameters of the content prediction model based on the comparing. In some cases, the one or more parameters may be adjusted in order to optimize a value calculated using a custom loss function. In one example, the custom loss function comprises a first component corresponding to embeddings and a second component corresponding to content identifiers, and the first component is parametrized to have a stronger impact on loss than the second component.” [0067]).
Regarding claims 7, 14, and 19, Lamba discloses: The computer-implemented method of claim 6, wherein generating the training data further comprises labeling one or more search query-content item record pairs based on (i) an occurrence of a selection of one or more training content items, or (ii) transaction data comprising the one or more training content items (“Operations 500A proceed to step 510 with training the machine learning model, using the training data set, to output corresponding embeddings (and, in some embodiments, identifiers) of relevant content items for users based on features of the users. In some embodiments, training the machine learning model includes providing the features of the plurality of users in the training data set as inputs to the machine learning model, comparing outputs received from the machine learning model in response to the inputs to the respective labels in the training data set, and iteratively adjusting one or more parameters of the content prediction model based on the comparing. In some cases, the one or more parameters may be adjusted in order to optimize a value calculated using a custom loss function. In one example, the custom loss function comprises a first component corresponding to embeddings and a second component corresponding to content identifiers, and the first component is parametrized to have a stronger impact on loss than the second component.” [0067], note: features of users can include clickstream data [0059]).
Regarding claims 8, 15 and 20, Lamba does not explicitly disclose, however, Chang teaches: The computer-implemented method of claim 1, wherein the plurality of content items is associated with a respective plurality of initial rankings, and assigning the plurality of rankings further comprises re-ranking the plurality of content items by modifying the plurality of initial rankings based on the plurality of predictions (Fig 2 top-k candidates, question answer re-rank, answer).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jin et al. (Medcpt: Contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retrieval), Du et al. (US 11768843), Mashrabov et al. (US 20200233903), and Venkataraman et al. (US 10049386) all disclose methods of content, search, retrieval and ranking.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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