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 1-11, 13-18, and 20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/20/2026 has been entered.
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 1-11, 13-18, and 20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process), claim 8 is directed to a system (i.e., machine), and claim 15 is directed to a computer program product (i.e., machine or manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8, and 15 (claim 1 being representative) is:
identifying information describing an invention;
defining a search space of prior art;
refining the search space based on a comparison between the invention and the prior art, a current cluster membership and at least one metadata comprising an intermediate rating and an associated confidence score, wherein the refining comprises:
reassigning the respective references and revising the at least one metadata in accordance with results of the comparison;
selecting at least some of the respective references for subsequent refining passes based at least in part on the stored intermediate rating and the associated confidence score; and
performing the subsequent refining passes until a computed threshold confidence score is reached in a threshold number of reference ratings;
determining a rating of a reference found in the refined search space; and
assessing a novelty of the invention based on the rating.
The abstract idea steps italicized above recite identifying information about inventions, assess novelty, and compare it to prior art, which could be performed mentally, including with pen and paper. This is further supported by paragraph [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim recites patent examination and invention novelty analysis, which constitutes a process that, under its BRI, covers commercial activities. This is further supported by paragraph [0003] of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: processor-implemented; using natural language processing, and further based on a heuristic algorithm; while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index.
Claim 8 recites the following additional elements: A computer system; one or more processors; one or more computer-readable memories; one or more computer-readable tangible storage media; program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories; using natural language processing, and further based on a heuristic algorithm; while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index.
Claim 15 recites the following additional elements: A computer program product; one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor capable of performing a method; using natural language processing, and further based on a heuristic algorithm; while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs [0016] – [0023] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claims 2, 9, and 16 include further additional elements with additional tasks that narrow the abstract idea: wherein the heuristic algorithm is a particle swarm optimization algorithm. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claims 3, 10, and 17 include further additional elements with additional tasks that narrow the abstract idea: wherein the natural language processing includes topic modeling. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claims 4, 11, and 18 include further additional elements with additional tasks that narrow the abstract idea: wherein the preparing is performed by a trained large language model. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Regarding claims 5-7, 13-14, and 20, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Accordingly, claims 1-11, 13-18, and 20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, 8, 13, 15, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Lepeltier (US 20170075877) in view of Okamoto (US 20240177255) in further view of Nachnani (US 20150032738).
Claims 1, 8, and 15
Regarding claims 1, 8, and 15 Lepeltier discloses:
(claim 1) A processor-implemented method, the method comprising {“There is disclosed a computer-implemented method of handling a patent claim” (paragraph 0006).}
(claim 8) A computer system, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising {“ The invention can take form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device” (paragraph 0250).}
(claim 15) A computer program product, the computer program product comprising: one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more computer-readable tangible storage media, the program instructions executable by a processor capable of performing a method, the method comprising {“The invention can take form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device” (paragraph 0250).}
identifying information describing an invention {The system receives subject matter input from the user, which includes invention disclosures, descriptions, or claims. The input is processed to understand the invention being evaluated (paragraphs 0083, 0085-0086).}
defining a search space of prior art; {Definitions from the specification or external sources (e.g., handbooks, claim dictionaries) are extracted and reinserted into the claim to form complete, searchable variants (i.e., the system defines and structures a contextual and terminological boundary) (paragraphs 0096, 0103).}
Lepeltier does not disclose:
refining the search space based on a comparison between the invention and the prior art using natural language processing, and further based on a heuristic algorithm, while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index, a current cluster membership and at least one metadata comprising an intermediate rating and an associated confidence score, wherein the refining comprises:
updating the cluster index by reassigning the respective references among clusters and revising the at least one metadata in accordance with results of the comparison;
selecting, using the updated cluster index, at least some of the respective references for subsequent refining passes based at least in part on the stored intermediate rating and the associated confidence score; and
performing the subsequent refining passes until a computed threshold confidence score is reached in a threshold number of reference ratings;
determining a rating of a reference found in the refined search space; and
assessing a novelty of the invention based on the rating.
However, Okamoto, in a similar field of endeavor directed to patent data management, teaches:
determining a rating of a reference found in the refined search space; and {References are selected into a refined set (e.g., Da and Db) based on semantic relevance, and a rating (i.e., individual score) is determined for each claim in light of those selected references (paragraphs 0202, 0233, 0235, 0238).}
assessing a novelty of the invention based on the rating. {Novelty is assessed using a calculated score based on semantic similarity between the claim and prior art. That rating structure reflects lack of novelty (paragraphs 0203, 0246-0247, 0255).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the artificial and computational handling of patent claims features of Lepeltier, to include the patent data management features of Okamoto, to suppress the unnecessary use of processing units and memory and provide information on the validity of industrial property rights in a prompt manner. (see paragraph [0007] of Okamoto).
The combination of Lepeltier and Okamoto does not teach:
refining the search space based on a comparison between the invention and the prior art using natural language processing, and further based on a heuristic algorithm, while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index, a current cluster membership and at least one metadata comprising an intermediate rating and an associated confidence score, wherein the refining comprises:
updating the cluster index by reassigning the respective references among clusters and revising the at least one metadata in accordance with results of the comparison;
selecting, using the updated cluster index, at least some of the respective references for subsequent refining passes based at least in part on the stored intermediate rating and the associated confidence score; and
performing the subsequent refining passes until a computed threshold confidence score is reached in a threshold number of reference ratings.
However, Nachnani, in a similar field of endeavor directed to matching and confidently adding snippets of search results to clusters of objects, teaches:
refining the search space based on a comparison between the invention and the prior art using natural language processing, and {The system supports refining and processing snippets and extracted information using NLP techniques to identify relevant data and improve matching operations. [0052]}
further based on a heuristic algorithm, {Heuristics and probabilistic scoring algorithms may be used for evaluating matches, confidence, and object associations. [0019], [0040]}
while maintaining in persistent storage a cluster index that stores, for respective references in the cluster index, a current cluster membership and at least one metadata comprising an intermediate rating and an associated confidence score, wherein the refining comprises {The system supports maintaining in persistent storage, indexed clusters of matching claims and records for person/object matching, where the cluster bolt 238 groups matching claims into common clusters, the merge bolt 240 computes confidence scores for merged records and profile attributes, and the persist bolt 244 stores the clustered records in the person database. [0029], [0045] – [0046], [0049] – [0050]}
updating the cluster index by reassigning the respective references among clusters and revising the at least one metadata in accordance with results of the comparison; {Clustered records are updated by determining when a data snippet matches multiple clusters, combing clusters of objects accordingly, and recalculating confidence scores and merged record information based on the matching results. [0023] – [0024], [0046], [0049]}
selecting, using the updated cluster index, at least some of the respective references for subsequent refining passes based at least in part on the stored intermediate rating and the associated confidence score; and {Snippets and clustered records are selected for additional matching and refinement operations based on calculated confidence scores, where snippets having sufficiently high confidence are added to clusters and other snippets are retained for later matching operations. [0022], [0025], [0044], [0046]}
performing the subsequent refining passes until a computed threshold confidence score is reached in a threshold number of reference ratings; {Matching and scoring operations are performed using computed confidence scores and threshold determinations as well, to decide whether snippets and records are accepted, merged, further processed, or reviewed. [0019], [0025] – [0026], [0040]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier and Okamoto to include the NLP matching and clustering of records features of Nachnani, to improve the accuracy and efficiency of identifying and refining relevant references to reduce false matches, and streamline selection of references for further analysis. (See paragraph [0019] of Nachnani).
Claims 6, 13, and 20
Regarding claims 6, 13, and 20, the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above. Lepeltier further discloses:
recommending a change in the invention based on the rating. {The system generates modified versions of a patent claim by applying structured variations (paragraphs 0079-0081). Each generated text can be associated with scoring or evaluation parameters. Differences between original and modified claims can guide further variations (paragraph 0094).}
Claims 2, 9, and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Lepeltier, Okamoto, and Nachnani in further view of Burns, Sr. (US 11232383).
Claims 2, 9, and 16
Regarding claims 2, 9, and 16, while the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above, it does not explicitly teach:
wherein the heuristic algorithm is a particle swarm optimization algorithm.
However, Burns, Sr., in a similar field of endeavor directed to automated work systems and methods for analyzing technology, intellectual property, and corporate management and structure for commercialization, teaches:
wherein the heuristic algorithm is a particle swarm optimization algorithm. {The system operates predictive modeling, including particle swarm optimization, in an intellectual property evaluation context (col. 81, line 47 – col. 82, line 1).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier, Okamoto, and Nachnani to include the intellectual property valuation features of Burns, Sr., to increase commercial impact success rates, commercial development rates and improve return on investment. (see Abstract of Burns, Sr.).
Claims 3-5, 10-11, and 17-18 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Lepeltier, Okamoto, and Nachnani in further view of Dhar (US 20240095268).
Claims 3, 10, and 17
Regarding claims 3, 10, and 17, while the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above, it does not explicitly teach:
wherein the natural language processing includes topic modeling.
However, Dhar, in a similar field of endeavor directed to a system and method for productivity improvements in document comprehension, teaches:
wherein the natural language processing includes topic modeling {The NLP processing includes topic extractions (paragraph 0069).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier, Okamoto, and Nachnani to include the document management and comprehension features of Dhar, to close the gap between the user's existing knowledge, the formulated question, and the available content data to locating the most pertinent responses in natural language queries. (see paragraph [0003] of Dhar).
Claims 4, 11, and 18
Regarding claims 4, 11, and 18, while the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above, it does not explicitly teach:
wherein assessing the novelty includes preparing a novelty report explaining the assessment of the novelty, wherein the preparing is performed by a trained large language model.
However, Dhar, in a similar field of endeavor directed to a system and method for productivity improvements in document comprehension, teaches:
wherein the assessing novelty includes preparing a novelty report explaining the assessment of the novelty, wherein the preparing is performed by a trained large language model {The system performs novelty assessments using trained large language models (e.g., BERT) and generates scores that form the basis of a novelty report (paragraphs 0042, 0064, 0072, 0074).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier, Okamoto, and Nachnani to include the document management and comprehension features of Dhar, to close the gap between the user's existing knowledge, the formulated question, and the available content data to locating the most pertinent responses in natural language queries. (see paragraph [0003] of Dhar).
Claim 5
Regarding claim 5, while the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above, it does not explicitly teach:
wherein the assessing further includes assessing a business value of the invention.
However, Dhar, in a similar field of endeavor directed to a system and method for productivity improvements in document comprehension, teaches:
wherein the assessing further includes assessing a business value of the invention. {The system has the ability to generate business-oriented insights from patent documents, including licensing potential, product risk, and SWOT analysis (i.e., business value) (paragraphs 0059, 0064).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier, Okamoto, and Nachnani to include the document management and comprehension features of Dhar, to close the gap between the user's existing knowledge, the formulated question, and the available content data to locating the most pertinent responses in natural language queries. (see paragraph [0003] of Dhar).
Claims 7 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Lepeltier, Okamoto, and Nachnani in further view of Kumar Rangarajan Sridhar (US 20160110343).
Claims 7 and 14
Regarding claims 7 and 14, while the combination of Lepeltier, Okamoto, and Nachnani teaches the limitations set forth above, it does not explicitly teach:
wherein defining the search space includes using a Gaussian mixture model to define one or more clusters in the search space.
However, Kumar Rangarajan Sridhar, in a similar field of endeavor directed to the unsupervised modeling and determination of short text topics, teaches:
wherein defining the search space includes using a Gaussian mixture model to define one or more clusters in the search space. {The system uses a Gaussian mixture model to group distributed word representations into clusters, where each cluster corresponds to a topic (paragraphs 0036, 0054).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lepeltier, Okamoto, and Nachnani to include the topic identification in an unsupervised manner features of Kumar Rangarajan Sridhar, to reliably identify latent topics in a topic model for large numbers of short texts. (see paragraph [0005] of Kumar Rangarajan Sridhar).
Response to Arguments
Applicant’s arguments filed on 03/19/2026 have been carefully considered.
Claim Rejections - 35 USC § 101
Applicant’s remarks under 35 USC § 101, while well taken, are not persuasive.
The amended claims remain directed to an abstract idea. Under the BRI, the limitations constitute mental processes and data evaluation techniques performed using generic computer components.
Applicant’s argument that the claims recite a “particular index technique” is unpersuasive because the claims do not recite a specific improvement to computer technology or a specific implementation of an indexing structure. The recited “cluster index”, “metadata”, “intermediate rating”, and “confidence score” merely represent information used in the abstract analytical process. Likewise, the recited updating, selecting, and terminating operations are generic data processing functions.
Applicant’s reliance on Enfish and Ex-parte Desjardins is unpersuasive because the claims do not improve the manner in which computers store or retrieve data, nor do they recite a specific technological solution to a technological problem. Instead, the claims use generic computer functionality to perform prior art analysis more efficiently.
Any alleged increase in speed, scalability, or reduction in cost results from applying the abstract idea on a computer and does not show an improvement to computer functionality itself. The claims do not recite a specific mechanism for achieving the alleged improvements.
Accordingly, the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to patentability under 35 U.S.C. § 103 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.
Regarding any arguments concerning the dependent claims, Examiner notes that they are predicated on the independent claims, which have been amended. For the same reason as above, these arguments are moot. Examiner directs Applicant’s attention to the claim analysis above.
In summary, examiner has responded to all arguments and found them unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20180075037, which teaches: In an aspect, a method includes receiving lists of entities, each list (i) having an associated score, (ii) being associated with a respective context fact, and (iii) ranking a subset of the entities, and for each of the lists of entities, generating, for each entity on the list, a data structure that references (i) the entity, (ii) the context fact associated with the list, (iii) the rank of the entity for the context fact, and (iv) the score for the list. The method can also include receiving data identifying a particular entity, selecting a particular data structure that references the particular entity, and providing, for output, data indicating (i) the context fact associated with the particular data structure that references the particular entity, and (ii) the rank of the entity for the context fact associated with the particular data structure that references the particular entity.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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