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 Amendment
In response to the office action mailed on 11/05/2026, applicant filed an amendment on 01/30/2026, amending claims 1, 3, 4, 8, 10, 11, 15, 17, and 18. The pending claims are 1-20.
Response to Arguments
With regard to 35 U.S.C 101 rejection, applicant's arguments filed 01/30/2026 have been fully considered but they are not persuasive.
Applicant argues that similar to Claim 2 of Example 37 of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), independent Claims 1, 8, and 15 of the present application cannot practically be performed in the human mind because the claims embrace generating and manipulating high-dimensional numerical data vectors using a weighting model, determining similarity scores between a query vector and a plurality of stored data vectors, ranking the data vectors based on the similarity scores, and generating stochastically distributed test vectors in a multi-dimensional vector space at a predetermined radius from the query vector. Such high-dimensional numerical vector operations and stochastic sampling operations represent a computationally intensive task that exceeds the capacity of mental processing or pen-and-paper calculation.
The examiner notes that the steps of generating and manipulating numerical data vectors, determining similarity scores between vectors and ranking the vectors, and generating stochastically distributed test vectors, encompass a mental process that may be practically performed in the human mind using observation, evaluation, judgment, and evaluation. As to the high-dimensional numerical vector operations and stochastic sampling operations, the examiner notes that high-dimensional numerical vectors are not claimed, and even if claimed, it is noted that neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1367 (Fed. Cir. 2015) (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
Applicant argues that similar to Claim 2 of Example 37 of the 2019 PEG, a processor accessing computer memory is required to perform these operations. Therefore, the claimed steps are not practicable as a mental process.
The examiner notes that determining the amount of use of each icon by tracking how much memory has been allocated to each application associated with each icon over a predetermined period of time necessarily requires a processor to access computer memory indicative of application usage, as there is no other way to perform this step. However, in our case, given the broadest reasonable interpretation, a vector can be generated by a human using mathematical relations. Same for the claimed comparing and ranking steps.
As to integrating the exception into a practical application, applicant argues that the pending claims recite an improvement over existing data record retrieval and search systems by referring to multiple paragraphs in the specification.
The examiner notes that for improvement, the claims themself should reflect the improvement in technology. The claims should cover a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. Determining a similarity score for data vectors; generating rankings for each of plurality of data records based on the first similarity scores; and selecting, based on the rankings, a grouping comprising a predetermined number of the data records with maximized first similarity scores is a well-known routine and does not provide improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
With regard to the prior art rejection, applicant’s arguments with respect to the pending claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
4. 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 non-statutory subject matter. 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: Is the claimed invention to a process, machine, manufacture or composition of matter?
The claimed invention, at independent claims 1, 8, and 15, is directed to a method (process), system (machine), and computer readable medium (manufacture) for retrieving a plurality of data records from a data repository; receiving a query statement from an endpoint device, wherein the query statement is provided to the endpoint device at a user interface; generating, using a weighting model comprising a Term Frequency-Inverse Document Frequency model, a data vector for each of the data records and a query vector for the query statement, wherein data vectors comprise numerical data vectors, and wherein query vectors comprise numerical query vectors, the data vectors and the query vectors corresponding to terms in a corpus stored in computer memory; determining a first similarity score for each data vector, wherein the first similarity score corresponds to a similarity between each data vector and the query vector; generating rankings for each of plurality of data records based on the first similarity scores; selecting, based on the rankings, a grouping comprising a predetermined number of the data records with maximized first similarity scores; and generating, using a stochastic algorithm based on a probabilistic distribution stochastically, test vectors centered from the query vector at a predetermined radius, wherein a quantity of test vectors generated is predetermined, and wherein the stochastic algorithm comprises sampling random values from the probabilistic distribution to compute coordinates of the test vectors relative to the query vector.
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Under the 35 U.S.C. 101 new guidelines, the broadest reasonable interpretation of the claims, the claimed steps fall within the “Mental Processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
The step of retrieving a plurality of data records from a data repository could be done by a human searching a database and retrieving data without using a machine. The steps of generating a data vector for each of the data records and a query vector for the query statement, determining a first similarity score for each data vector, and generating rankings and grouping the plurality of data records, may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, a human can manually convert text data into numerical representation or vectors, compare the vector to determine a similarity score, rank and group them based on the determined similarity score. For the step of generating test vectors centered from the query vector at a predetermined radius, a human can select vectors that is close to the query vector at a predetermined distance as test vectors without using a machine. See MPEP 2106.04(a)(2), subsection III. Therefore, the claimed steps fall within the mental process grouping of abstract ideas
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of “a processing device”, “machine learning”, “weighting model”, “endpoint device” are mere data gathering and manipulating recited at high level of generality, and thus are insignificant extra-solution activity The processor is recited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recitation of “machine learning” recited at the preamble is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
As to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (Step 2B), as explained above in Step 2A, Prong 2, the use of “machine learning”, “processor” is at high level of generality, and even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Accordingly, the claims are ineligible.
Dependent claims 2-7, 9-14, and 16-20 further refer and describe the process of determining subsequent similarity scores between subsequent vectors, ranking, and generating subsequent test vectors, which encompasses a mental process that is practically performed in the human mind, as explained above in Step 2A, Prong 1. The step of transmitting the query results to the endpoint device for display on the user interface of the endpoint device (claims 5, 12, 19) is considered as data gathering and manipulating, which is insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional.
Accordingly, claims 1-20 are directed to an abstract idea, and are not patent eligible.
Claim Rejections - 35 USC § 103
5. 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.
Claims 1-2, 5, 7-9, 12, 14-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Colgan (US 20250077793) in view of Deng (WO 2021169288, published 09/02/2021), and further in view of Clifton (US 20230410793).
As per claim 1, Colgan teaches, at Fig. 1, a processing device; and a non-transitory storage device comprising instructions that, when executed by the processing device, causes the processing device to perform:
retrieving a plurality of data records from a data repository ([0045], wherein the computing system can efficiently “query” the vector database by performing a nearest-neighbor search, or ANN search);
receiving a query statement from an endpoint device, wherein the query statement is provided to the endpoint device at a user interface (Fig. 2, [0044], a computing system 202 can obtain a query 204);
generating, using a weighting model, a data vector for each of the data records and a query vector for the query statement (Fig. 2, [0018], [0044], [0045], wherein the computing system can process the received query with a vector determinator to obtain a query vector. The query vector can be a vectorized representation of the query, and wherein the vector database comprises a plurality of embeddings for a respective plurality of vector representations of data items);
determining a first similarity score for each data vector, wherein the first similarity score corresponds to a similarity between each data vector and the query vector ([0035], [0037], the embedding search module can then map the query embedding to the embedding space and perform a nearest neighbor search to identify one or more result embeddings most similar to the query embedding. The embeddings that their similarity score or distance is within a threshold value);
generating rankings for each of plurality of data records based on the first similarity scores ([0018], [0035], [0059], the process of determining the most similar embeddings to the embeddings of the query based on relative distance ranking);
selecting, based on the rankings, a grouping comprising a predetermined number of the data records with maximized first similarity scores ([0035], the embedding search module may identify any result embeddings that are within a threshold distance of the query embeddings within the embedding space. For another example, the embedding search module may identify a certain number of result embeddings closest to the query embedding in the embedding space); and
generating, stochastically, test vectors centered from the query vector at a predetermined radius (identifying a result of vectors within a threshold distance of the query embedding within the embeddings space. The embedding space 55 can be a component of the vector database 18, or may be included in the vector database, [0018], [0035]- [0036]).
Colgan may not explicitly disclose wherein a quantity of test vectors is predetermined. Deng in the same field of endeavor relates to the field of artificial intelligence semantic understanding model training method and apparatus, wherein a preset number of continuous word vectors is randomly selected as test vectors (Abstract). Therefore, it would have been obvious at the time the application was filed to use Deng’s feature of selecting a preset number of word vectors as test vectors with the system of Colgan, in order to test a wide range of data and improve result quality.
As to using a Term Frequency-Inverse Document Frequency model for generating the data vector, and using a stochastic algorithm based on a probabilistic distribution for generating test vectors, wherein the stochastic algorithm comprises sampling random values from the probabilistic distribution to compute coordinates of the test vectors relative to the query vector, Colgan in view of Deng may not explicitly disclose these features. However, Clifton in the same field of endeavor teaches generating data vectors using a weighting model comprising a Term Frequency-Inverse Document Frequency model (Clifton, [0080], wherein sentences are converted into vector representations using (TF-IDF), and using a stochastic algorithm, wherein the stochastic algorithm comprises sampling random values from the probabilistic distribution to compute coordinates of the test vectors relative to the query vector (Clifton, [0074], wherein a stochastic algorithm is used for generating vector representations and [0115], wherein random sampling is performed. Also, the examiner notes that a stochastic algorithm is a computational method that incorporates randomness—such as random sampling into its steps).
Therefore, it would have been obvious at the time the application was filed to use Clifton’s above features with the system of Colgan in view of Deng, in order to generate the claimed data vectors using a weighting model comprising a Term Frequency-Inverse Document Frequency model and generate the claimed test vectors using a stochastic algorithm based on a probabilistic distribution. This would provide a powerful approach for words evaluation and data representation.
As per claim 2, Colgan may not explicitly disclose determining a second similarity score for each of the test vectors, wherein the second similarity scores correspond to a similarity between each test vector and each data vector of the grouping; determining a subsequent query vector comprising the test vector comprising a highest second similarity score of each of the test vectors and comparing metrics to a predetermined stopping criteria. However, Colgan teaches training the disclosed machine learning models iteratively based on sets of training data to update the models’ parameters over a number of training iterations ([0068]- [0069]). Therefore, it would have been obvious at the time the application was filed for the system of Colgan to determine a second similarity score for each of the test vectors, wherein the second similarity scores correspond to a similarity between each test vector and each data vector of the grouping; determine a subsequent query vector comprising the test vector comprising a highest second similarity score of each of the test vectors and comparing metrics to a predetermined stopping criteria. This would reduce errors and improve quality.
As per claim 5, Colgan teaches determining a final ranking of the data records; providing the final ranking of the data records to a large language model as a few- shot input to output as query results, wherein the query results are ranked based on the few- shot input; and transmitting the query results to the endpoint device for display on the user interface of the endpoint device ([0114]- [0116], wherein processing the query and the one or more data items comprises processing the query and the one or more sets of textual content with a large language model (LLM) to obtain a textual output).
As per claim 7, Colgan teaches training the disclosed machine learning models iteratively ([0068]- [0069]). More, Colgan teaches a computing device for obtaining user’s queries, generating and processing the vector representation of the query with a machine-learned embedding model, and determining semantic similarity scores between vectors based on the relative distance between the queries vectors and the database vectors (Fig. 2, [0044]- [0046]). Therefore, it would have been obvious at the time the application was filed for the system of Colgan to perform measuring between at least one of the second similarity score, and a third similarity score corresponding to a similarity between each of the test vectors and the new query vector, in order to improve result quality.
As per claims 8-9, 12, and 14, Colgan teaches a computer readable medium ([0005]). The remaining steps are rejected under the same rationale as applied to the method steps of rejected claims 1-2, 5, 7.
As per claim 15, 16, 19, method claim 15, 16, 19 and apparatus claims 1, 2, 5 are related as method and apparatus of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 15, 16, 19 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1, 2, 5.
Claims 3-4, 10-11, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Colgan in view of Deng and Clifton, and further in view of Sharma (US 2022/0229994).
As per claims 3, 10, and 16, Colgan may not explicitly disclose wherein upon a condition in which the metrics do not satisfy the predetermined stopping criteria, the instructions further cause the processing device to perform an iterative loop, the iterative loop comprising steps of: generating, stochastically, subsequent test vectors centered from the subsequent query vector at a predetermined radius, wherein a quantity of the subsequent test vectors generated is predetermined; determining a similarity score for each of the subsequent test vectors, wherein the similarity score corresponds to a similarity between each subsequent test vector and each data vector of the grouping; and determining a new subsequent query vector comprising a subsequent test vector comprising a highest similarity score of each of the subsequent test vectors; comparing new metrics to the predetermined stopping criteria. However, triggering predetermined stopping conditions is well known in the art as evidenced by Sharma. Sharma in the same field of endeavor teaches a natural language understanding (NLU) framework that includes a configuration vector storing settings of various components that may be applied during NLU inference of an utterance, wherein language-specific conditions, discourse-style conditions, and so forth, which govern when and how these rule-sets should be applied during the augmentation process ([0172], [0184], [0193]). As to the iterative process, it is a well-known routine, as evidenced by Sharma’s paragraphs [0061], [0106], [0160]. Therefore, it would have been obvious at the time the application was filed to use the triggered conditions of Sharma with the system of Colgan in view of Deng, in order perform the claimed steps and make better decisions and perform different actions based on criteria.
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As per claims 4, 11, and 18, Colgan may not explicitly disclose repeating the iterative loop until the new metrics satisfy the predetermined stopping criteria. Sharma in the same field of endeavor teaches repeating the conditional performing until the new metrics satisfy the predetermined stopping criteria ([0282]). As to the iterative process, it is a well-known routine, as evidenced by Sharma’s paragraphs [0061], [0106], [0160]. Therefore, it would have been obvious at the time the application was filed to use the Sharma’s above features with the system of Colgan in view of Deng, in order to perform the claimed step. This would improve problem-solving and provide satisfactory results.
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Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Colgan in view of Deng and Clifton, and further in view of Madnani (US 2025/0045314).
As per claim 6, 13, and 20, Colgan may not explicitly disclose wherein the first similarity score and the second similarity score are cosine similarity scores. Madnani in the same field of endeavor teaches determining vectors similarity based on cosine similarity scores ([0018], [0019], [0030]). Therefore, it would have been obvious at the time the application was filed to use the cosine function of Madnani with the system of Colgan in view of Deng, in order to provide reliable metric even in high-dimensional environments.
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Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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 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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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELALI SERROU whose telephone number is (571)272-7638. The examiner can normally be reached M-F 9 Am - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659
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