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 .
The preliminary amendment filed on 5/16/2025 has been acknowledged and entered.
Claims 1-20 are cancelled.
Claims 21-30 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of U.S. Patent No. 12,326,883 (Application No. 18/498,936).
Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to similar invention with similar limitations as demonstrated in the table below:
Claims 21-24 of instant application recite similar limitations as claim 25-30, hence claims 21-24 are being used as representative for demonstration in the table below.
Similarly, claims 1-2 of U.S. Patent No. 12,326,883 recite similar to limitations as claims 3-6. Hence claims 1-2 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 12,326,883
21. A method comprising:
receiving, at a classification data store, a plurality of subject text strings, a plurality of associated class text strings, and a relationship of a description of a class of each class text string, the plurality of subject text strings each comprising a name, the class text strings each describing the class;
generating, by a machine learning model, a subject vector embedding based on each of the plurality of subject text strings and a class vector embedding based on each of the plurality of class text strings;
receiving, at a scoring engine from the machine learning model and as input to a binary search process, each of the subject vector embeddings and each of the class vector embeddings;
generating, by the scoring engine, a similarity score, wherein the similarity score is a measurement of similarity between the subject vector embedding and the class vector embedding;
determining, by the scoring engine, that the similarity score is below a threshold value;
splitting, by the scoring engine, the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings;
receiving, by the scoring engine, a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings;
calling, by the scoring engine, the binary search process using the new subject vector embedding and the class vector embedding as input to the binary search process;
generating, by the scoring engine executing the binary search process and from the classification data store, two or more subject text strings of the plurality of subject text strings that are concatenated with a separation character and removing the separation character; and
22. The method of claim 21, further comprising: wherein the one class text string is associated with the class vector embedding.
generating, by a large language model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings as a lookup key, the large language model determining a subject text string from the query is similar to the one class text string.
24. The method of claim 21, further comprising: appending, by the large language model, contextual information about one subject text string of the plurality of subject text strings by searching public information on a website.
23. The method of claim 21, wherein the plurality of subject text strings are split at one of the separation characters between each subject text string of the plurality of subject text strings.
1. A method comprising:
receiving, at a classification data store, a plurality of subject text strings, a plurality of associated class text strings, and a relationship of a description of a class of each class text string, the plurality of subject text strings each comprising a name of an organization, the class text strings each comprising a merchant category code that describe the class;
generating, by a machine learning model, a subject vector embedding based on each of the plurality of subject text strings and a class vector embedding based on each of the plurality of class text strings;
receiving, at a scoring engine from the machine learning model and as input to a binary search process, each of the subject vector embeddings and each of the class vector embeddings;
generating, by the scoring engine, a similarity score, wherein the similarity score is a measurement of similarity between the subject vector embedding and the class vector embedding;
determining, by the scoring engine, that the similarity score is below a threshold value;
splitting, by the scoring engine, the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings;
receiving, by the scoring engine, a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings;
calling, by the scoring engine, the binary search process using the new subject vector embedding and the class vector embedding as input to the binary search process;
generating, by the scoring engine executing the binary search process and from the classification data store, two or more subject text strings of the plurality of subject text strings that are concatenated with a separation character and removing the separation character;
providing, from the scoring engine and to a large language model, the concatenated string and one class text string of the class text strings, the one class string being associated with the class vector embedding;
generating, by the large language model in communication with the classification data store and as a result of a query, the concatenated string using the one class text string as a lookup key, the large language model determining a subject text string from the query is similar to the one class text string.
2. The method of claim 1, wherein the plurality of subject text strings are split at one of the separation character between each subject text string of the plurality of subject text strings.
As demonstrated by the mappings in the table above, U.S. Patent No 12,326,883 discloses or renders obvious all the features of the claims of the instant application.
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 25-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
Independent claim 25 recites a mental process in the limitations of “…receiving…plurality of subject text strings… generating…a first subject vector embedding from the text string…generating…a similarity score….determining…class text string…determining…a most common related class text string……mapping… a relation from the first subject text string to the most common related class text string…” These limitations could be done mentally with data evaluations based on gathered information. Mental process is directed to one of the abstract ideas groups as set forth by Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. subject text strings, a plurality of associated class text strings, and a relationship of a description of a class) are directed to types of information materials, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claim does not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such as machine learning model, scoring engine) in the claim amount to no more than usage of a generic computing components in a generic computing system, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Dependent claim 26 further recites an additional mental process in a limitation of “…generating, by a large language model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings…” which correspond to creating an output functor based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g.a result of a query, class text string ) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Dependent claim 27 recites an additional mental process in a limitation of “appending, by the large language model, contextual information about one subject text string…” which correspond to adding information based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g. contextual information, subject text string) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Independent claim 28 recites a mental process in the limitations of “…receiving…plurality of subject text strings… generating…a first subject vector embedding …receiving…each of the subject vector embeddings…generating…a similarity score….determining… that the similarity score is above a threshold value…projecting the similarity…determining…the text strings are correct based on the projection” These limitations could be done mentally with data evaluations based on gathered information. Mental process is directed to one of the abstract ideas groups as set forth by Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance.
The limitation of “storing an updated mapping…” is directed to an insignificant extra-solution activity at Step 2A Prong Two, and also would be well-understood, routine, and conventional at Step 2B. This is nothing more than providing information, and does not provide any integration into a practical application
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. a plurality of subject text strings, a plurality of associated class text strings, and a relationship of a description of a class) are directed to types of information materials, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claim does not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such as machine learning model, scoring engine) in the claim amount to no more than usage of a generic computing components in a generic computing system, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Dependent claim 29 further recites elements describing additional data elements (i.e. the plurality of subject text strings are split at one of the separation characters between each subject text string…) in the limitation, and such additional elements are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception.
Dependent claim 30 recites an additional mental process in a limitation of “…generating, by a large language model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings…” which correspond to creating an output functor based on gathered data information, which could be performed mentally based on the gathered information. The additional elements (e.g. a result of a query, class text string ) in the limitation are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception. Further, the component components (e.g. large language model) are directed to generic usage of generic computing components to provide output based on the gathered information.
Thus, for at least the reasoning above, the pending claims are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 25-27 and 30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “most common” in claim 25 is a relative term which renders the claim indefinite. The term “most common” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 26 further recites the limitation "the concatenated string”, in addition to incorporate the deficiency of claim 25 stated above. There is insufficient antecedent basis for this limitation in the claim.
Claim 30 recites the limitation "the concatenated string”. There is insufficient antecedent basis for this limitation in the claim.
Dependent claim 27 depending from claim 25 is rejected for incorporate the deficiency of claim 25 stated 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 (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 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.
Claim 25, 28 and 29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kale et al (Pub No. US 2018/0329999, hereinafter Kale)
Kale is cited in the IDS filed on 5/15/2025.
With respect to claim 25, Kale discloses a method (abstract) comprising:
receiving, at a classification data store, a plurality of subject text strings, a plurality of associated class text strings, and a relationship of a description of a class of each class text string, the plurality of subject text strings each comprising a name, the class text strings each describing the class( the limitations of “the plurality of subject text strings each comprising a name, the class text strings each describing the class” are directed non-functional data material described by the subject text string class text string, which do not impact the functionality of the claimed steps; [0027-0028]: receive a plurality of text string represented by the tokens, class text string represented by a group of token and a relationship of description at a classification data store represented by a data store of a classifier such as an training database 205 or annotation database 208 as shown in Fig 2 and further described in [0028-0035]. The groups of token and relationships with names or attribute are further described via cluster analysis disclosed in [0064]);
generating, by a machine learning model, a first subject vector embedding from a first subject text string of the plurality of subject text strings that are in a same classification scheme as the first subject vector embedding ([0027], Fig 2: generate a first subject vector embedding represented by a vector of a group from a first token correspond to a first string of the text strings in a same classification schema as set forth by the classifier 235 via machine training model, as further described in [0042-0044], [0063]);
generating, by a scoring engine, a similarity score, wherein the similarity score is a measurement of similarity between the first subject vector embedding and a plurality of vector embeddings ([0028], Fig 5A: determine a similarity score represented a similarity measure of the vector via distance, as further disclosed [0031], [0072-0073];
determining, by the scoring engine, a plurality of class text strings associated with each of the plurality of vector embeddings respectively (all elements in a same system are associated; [0027-0028], Fig3A: determine that the class text strings represented by the token in a group/segments are associated with each respective vector, as a vector is generated for each token as further disclosed in [0040-0042 ;
determining, by the scoring engine, a most common related class text string among the plurality of class text strings ([0028], Fig 3B: determine a most common related text storing based on the similarity score when determine which segment token being part of, which is further described in [0041-0042], [0047] and [0046]); and
mapping, by the scoring engine, a relation from the first subject text string to the most common related class text string in the classification data store ([0038], Fig 4: mapping a relation via grouping or clustering from the token correspond to the text string to to the most common class in the classification data store represented by the training database or annotation database, as further described in [0042], [0064]).
With respect to claim 28, Kale discloses a method (abstract) comprising:
receiving, at a classification data store, a plurality of subject text strings, a plurality of associated class text strings, and a relationship of a description of a class of each class text string, the plurality of subject text strings each comprising a name, the class text strings each describing the class ( the limitations of “the plurality of subject text strings each comprising a name, the class text strings each describing the class” are directed non-functional data material described by the subject text string class text string, which do not impact the functionality of the claimed steps; [0027-0028]: receive a plurality of text string represented by the tokens, class text string represented by a group of token and a relationship of description at a classification data store represented by a data store of a classifier such as an training database 205 or annotation database 208 as shown in Fig 2 and further described in [0028-0035]. The groups of token and relationships with names or attribute are further described via cluster analysis disclosed in [0064]);
generating, by a machine learning model, a subject vector embedding based on each of the plurality of subject text strings and a class vector embedding based on each of the plurality of class text strings ([0027], Fig 2: generate a subject vector embedding—which is merely a vector embedding-- represented by a vector of a based in each of the tokens representing the subject text string, and a class vector represented by a different vector based the other token correspond to the class text string as set forth by the classifier 235 via machine training model, as further described in [0042-0044], [0063]);
receiving, at a scoring engine from the machine learning model and as input to a binary search process, each of the subject vector embeddings and each of the class vector embeddings ([0027-0028]: receiving at a scoring engine and as input to a binary search process represent by binary classifier, the vectors correspond to the subject vector embedding and each class vector embedding, [0042-0044], [0063]);
generating, by the scoring engine, a similarity score, wherein the similarity score is a measurement of similarity between the subject vector embedding and the class vector embedding ([0028], Fig 5A: determine a similarity score represented a similarity measure of the vector via distance, as further disclosed [0031], [0072-0073];
determining, by the scoring engine, that the similarity score is above a threshold value representing a similarity ([0031]: determine that the similarity score is above a threshold representing the similarity, as further described in [0064], [0072-0073]) ;
projecting, by the scoring engine, the similarity onto a classification verification scheme ([0030-0031]: project the similarity on a classification verification schema represented by the accuracy verification, as further described in [0064]);
determining, by the scoring engine, that the plurality of associated class text strings are correct based on the projection [0030-0031]: determine that class text string are correct based on the projection in view of the accuracy and likely segment determination, as further described in [0042], [0063-0064]); and
storing, by the scoring engine, an updated mapping of the relationship of the description of the class of each class text string based on the correctness determination ([0034-0035], Fig 2 & 3B: store an updated mapping of relationship via machine learning process with the training database or annotation database, as further described in [0046-0047]).
With respect to claim 29, Kale further discloses wherein the plurality of subject text strings are split at one of the separation characters between each subject text string of the plurality of subject text strings (the limitation is directed to non-functional descriptive material describing the text strings, [0027], Fig 1-2: split the text string represented by the partitions tokens at separation characters that separate the token, as further described in [00332]).
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.
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 26-27 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Kale, as applied to claims 25 & 28, in view of Palumbo (Pub No. US 2024/0386219, hereinafter Palumbo).
With respect to claims 26 and 30, Kale further discloses generating, by a machine learning model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings as a lookup key ([0027-0028]: generate a concatenated token representing the concatenated string using the tokens correspond to the class text string as a lookup key, as further disclosed in [0034, [0041-0042]).
Kale does not explicitly disclose generating, by a large language model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings as a lookup key, the large language model determining a subject text string from the query is similar to the one class text string.
However, Palumbo discloses generating, by a large language model in communication with the classification data store and as a result of a query, the concatenated string using one class text string of the class text strings as a lookup key, the large language model determining a subject text string from the query is similar to the one class text string ([0087-0089]: generate a the concatenated string using one class text string, e.g. concatenated item title and artist name by a large language model LLM in communication with the classification datastore as shown in Fig 4A-4C, wherein he LLM determining a subject string from the query is similar to the class represented by the subject and class of items via searching, as further disclosed in [0059-0062] & [0067-0068]).
Since both Kale and Palumbo are from the same field of endeavor because both are directed to text string processing with respect to similarity determination via machine learning, , which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings by incorpaote usage of a large language model in text string management of Palumbo into Kalo for manage the text string as claimed. The motivation to combine is to enhance information retrieval for users (Kale, [0003]; Palumbo, [0003]).
With respect to claim 27, the combined teachings of Kale and Palumbo further disclose
appending, by the large language model, contextual information about one subject text string of the plurality of subject text strings by searching public information on a website (Palumbo, [0059-0060]: appending contextual information about the subject text string via descriptors by a LLM, as further disclosed in [0067-0069]).
Examiner Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Owyang whose telephone number is (571)270-1254. The examiner can normally be reached Monday-Friday, 8am-6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at (571)272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE N OWYANG/Primary Examiner, Art Unit 2168
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