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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 06/21/2024, 09/02/2025, and 01/02/2026 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statements are being considered by the examiner.
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 15-20 are rejected under 35 U.S.C. 101 because the claims are drawn to a “signal” per se as recited in the preamble and as such is non-statutory subject matter. In paragraph [0053] of the As Filed Specification, the term “computer storage medium” is defined. However, “computer-readable medium" is not defined as to what the scope of the term is meant to encompass. Hence, one of ordinary skilled in the art can interpret such term to include transitory signals and non-transitory signals. It does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in § 101. First, a claimed signal is clearly not a "process" under § 101 because it is not a series of steps. The other three § 101 classes of machine, compositions of matter and manufactures "relate to structural entities and can be grouped as 'product' claims in order to contrast them with process claims." 1 D. Chisum, Patents § 1.02 (1994). The Applicant' s Specification presents a broad definition as to what the “computer-readable medium” covers and is being made to include transitory and non-transitory signals. The Applicant' s As Filed Specification in paragraph [0053], refers to the “computer storage medium”. Hence, it appears that the claims appear to be drawn towards transitory signals, which is not subject matter eligible. In order to overcome the present rejection, the Applicant is advised to amend the claims by using the following terminology: "non-transitory machine readable storage medium." Such example terminology has been also found in the Official Gazette 1351 OG 212.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 8, and 15 relate to the statutory category of method/process and machine/apparatus. The independent claim 1 recites “receiving a text data; performing text processing on the text data, the text processing including: converting text in the text data to string format; and applying tokenization to the converted text; performing sentence embedding on each token generated from the tokenization, the sentence embedding including converting sentences to fixed sized vectors in a vector space; generating a plurality of clusters by applying a clustering model to the converted sentences; for each cluster in the plurality of clusters: determining a cluster centroid; determining nearest neighbors to the cluster centroid; determining a similarity score between a number of the nearest neighbors; comparing the similarity score to a similarity threshold; upon the similarity score being less than the similarity threshold, generating a first summary of the cluster by applying abstractive summarization to the defined number of the nearest neighbors; and upon the similarity score being greater than the similarity threshold, generating a second summary of the cluster by selecting the nearest neighbor from the nearest neighbors as the second summary”.
With respect to claim 8, the claim recites “receiving text data; performing text processing on the text data, the text processing including: converting text in the text data to string format; and applying tokenization to the converted text; performing sentence embedding on each token generated from the tokenization; generating a plurality of clusters by applying a clustering model to the converted sentences; for each cluster in the plurality of clusters: determining a cluster centroid; determining nearest neighbors to the cluster centroid; determining a similarity score between a defined number of the nearest neighbors; comparing the similarity score to a similarity threshold; and upon the similarity score being less than the similarity threshold, generating a first summary of the cluster by applying abstractive summarization to the defined number of the nearest neighbors; and upon the similarity score being greater than the similarity threshold, generating a second summary of the cluster by selecting the nearest neighbor from the nearest neighbors as the second summary”.
With respect to claim 15, the claim recites “receiving a text data; performing text processing on the text data, the text processing including: converting text in the text data to string format; and applying tokenization to the converted text; performing sentence embedding on each token generated from the tokenization; generating a plurality of clusters by applying a clustering model to the converted sentences; for each cluster in the plurality of clusters: determining a cluster centroid; determining a defined number of nearest neighbors to the cluster centroid; and generating a summary of the cluster by applying an abstractive summarization to the defined number of the nearest neighbors”.
The limitations of claims 1 and 8 of “receiving…”, “performing…”, “converting…”, “applying…”, “performing…”, “generating…”, “…determining…”, “determining…”, “determining…”, “comparing…”, “…generating…”, and “…generating…”. As drafted covers mental activity. More specifically, for claim 1, a human after receiving textual data, can parse the data into a sequence of shorter segments that are then converted into numerical representations. A mathematical algorithm is then used to analyze the data and determine the similarity between the data and grouping the similar data items which meet a particular threshold together. A summary is then generated using the similarity data.
The limitations of claim 15 of “receiving…”, “performing…”, “converting…”, “applying…”, “performing…”, “generating…”, “…determining…”, “determining…”, and “…generating…”. As drafted covers mental activity. More specifically, for claim 1, a human after receiving textual data, can parse the data into a sequence of shorter segments that are then converted into numerical representations. A mathematical algorithm is then used to analyze the data and determine the similarity between the data and grouping the similar data items which meet a particular threshold together.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 1 and 15 recite the additional elements of “processor” and “memory” which are recited generally in the specification. For example, in paragraph [0052] of the as filed specification, there is a description of using a general purpose computing apparatus. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, 9, and 16, the claims relate to converting the textual data into numerical representations where the numerical representations relate to meaning and grammatical relationships of the textual data. The claims relate to a mental activity of using a mathematical algorithm to transform textual data. No additional elements are present.
With respect to claims 3, 4, 10, 11, 17 and 18, the claims relate to determining the closest groupings of textual data. The claims relate to using a mathematical formula to determine how closely the data grouping are. No additional elements are present.
With respect to claims 5, 12 and 19, the claims relate to determining the similarity between the closest groupings of textual data. The claims relate to using a mathematical formula to determine the similarity. No additional elements are present.
With respect to claims 6 and 13, the claims relate to determining if the similarity is between a particular threshold. The claims relate to using a mathematical formula to determine the similarity. No additional elements are present.
With respect to claims 7 and 14, the claims relate to determining if the different groupings of data relate to different topics. The claims relate to using a mathematical formula to determine the similarity. No additional elements are present.
With respect to claim 20, the claim relates to determining if the similarity is between a particular threshold and determining if the different groupings of data relate to different topics. The claim relates to using a mathematical formula to determine the similarity. No additional elements are present.
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.
Claims 8, 10-12, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nieborowski et al. (US 2023/0315993) in view of Bar Eliyahu et al. (US 11,983,489).
Regarding Claim 8, Nieborowski et al teaches a computerized method comprising:
receiving text data (generates metadata for the audio file (e.g., or text generated therefrom) that identifies subsets of the audio file) (page 8, paragraph [0109]);
performing text processing on the text data, the text processing including: converting text in the text data to string format (The communication service 204 can generate or retrieve one or more text strings based on metadata corresponding to a natural language input and update the natural language input to include the metadata-derived text strings) (page 8, paragraph [0108]); and
applying tokenization to the converted text (The communication service 204 can receive or determine classifications or tags with which a natural language item is associated and store the natural language data item in association with the classification or tag. The model service 207 may utilize classifications and/or tags as an additional variable from which similarity scores are determined) (page 8, paragraph [0108]);
performing sentence embedding on each token generated from the tokenization (The system can generate natural language vectors transforming textual data into a numerical form by applying one or more embedding techniques (for example, word2vec, fasttext, Infersent, or Google universal sentence encoder)) (page 6, paragraph [0090]);
generating a plurality of clusters by applying a clustering model to the converted sentences (The system can generate groupings of vectors (referred to as “clusters”) by grouping similar vectors (e.g., vectors that are close in distance when plotted to a virtual space) (page 6, paragraph [0090]);
for each cluster in the plurality of clusters: determining a cluster centroid (determining a central point of the vector representations and defining the cluster as a sphere of predetermined diameter extending from the centroid) (page 6, paragraph [0090]);
determining nearest neighbors to the cluster centroid (The system 200 can perform vector-cluster comparisons by computing distance metrics 109, 111 between the input vector 103 and each semantic cluster 105, 107. For example, to generate the distance metric 109, the system 200 computes a squared Euclidean distance between the input vector 103 and a centroid of the semantic cluster 105) (page 7, paragraph [0099]);
determining a similarity score between a defined number of the nearest neighbors (The system 200 can determine, for example, that the distance metric 111 is less than the distance metric 109 and, thus, the input vector 103 demonstrates greater similarity to the semantic cluster 107 as compared to the semantic cluster 105) (page 7, paragraph [0099]);
comparing the similarity score to a similarity threshold (In some embodiments, the system 200 compares the distance metrics 109, 111 to a predetermined similarity threshold (for example, a maximum distance value) to determine vector-cluster matching) (page 7, paragraph [0099]);
upon the similarity score being less than the similarity threshold, generating a first summary of the cluster to the defined number of the nearest neighbors (In one example, the communication service 204 generates a report including communication data 212, identifications of one or more clusters to which the communication data 212 was matched, and a confidence metric corresponding to the cluster match (for example, a distance metric or a comparison between the distance metric and a predetermined matching threshold). The communication service 204 can flag, highlight, or otherwise identify natural language inputs and subsets thereof for further review by a user) (pages 8 and 9, paragraph [0110]); and
upon the similarity score being greater than the similarity threshold, generating a second summary of the cluster by selecting the nearest neighbor from the nearest neighbors as the second summary. (This option is not selected with regards to ex parte Schulhauser. Both thresholds cannot be met at the same time.)
Nieborowski et al fails to teach generating a first summary of the cluster by applying abstractive summarization.
Bar Eliyahu et al teaches generating a first summary of the cluster by applying abstractive summarization (abstractive summary models aim to generate summaries that do not necessarily contain exact phrases or sentences from the original text, but instead capture the main ideas and concepts presented in the text) (col. 2, line 65-col. 3, line 1).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski with the teachings of Bar Eliyahu to speed up communication and improving natural language processing by generating an abstractive summarization report which captures just the main ideas in the textual data and requires smaller datasets.
Regarding Claim 10, Nieborowski et al teaches the method, wherein determining the nearest neighbors is in terms of Euclidean distance (he distance metric or similarity metric can include, but is not limited to, Euclidean distance, squared Euclidean distance) (page 10, paragraph [0019]).
Regarding Claim 11, Nieborowski et al teaches the method, wherein the nearest neighbors are within a threshold distance from the cluster centroid (For example, the model service 207 can compare a vector to one or more clusters to determine if the vector demonstrates threshold-satisfying similarity or distance to a cluster. The model service 207 can perform a comparison by computing a distance metric or similarity metric between the vector and each cluster (e.g., in particular, the centroid of each cluster)) (page 10, paragraph [0119]).
Regarding Claim 12, Nieborowski et al teaches the method, wherein determining the similarity score between the number of the nearest neighbors comprises applying a cosine similarity (calculate a distance between the current iteration data item and the cluster using a cosine similarity calculation) (page 5, paragraph [0069]).
Regarding Claim 15, Nieborowski et al teaches a computer-readable medium (Embodiments within the scope of the present disclosure also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon) (page 22, paragraph [0221]) comprising computer-executable instructions that, when executed by a processor (a computing device including a processing unit) (page 23, paragraph [0225]), cause the processor to perform the following operations:
receiving a text data (generates metadata for the audio file (e.g., or text generated therefrom) that identifies subsets of the audio file) (page 8, paragraph [0109]);
performing text processing on the text data, the text processing including: converting text in the text data to string format (The communication service 204 can generate or retrieve one or more text strings based on metadata corresponding to a natural language input and update the natural language input to include the metadata-derived text strings) (page 8, paragraph [0108]); and
applying tokenization to the converted text (The communication service 204 can receive or determine classifications or tags with which a natural language item is associated and store the natural language data item in association with the classification or tag. The model service 207 may utilize classifications and/or tags as an additional variable from which similarity scores are determined) (page 8, paragraph [0108]);
performing sentence embedding on each token generated from the tokenization (The system can generate natural language vectors transforming textual data into a numerical form by applying one or more embedding techniques (for example, word2vec, fasttext, Infersent, or Google universal sentence encoder)) (page 6, paragraph [0090]);
generating a plurality of clusters by applying a clustering model to the converted sentences (The system can generate groupings of vectors (referred to as “clusters”) by grouping similar vectors (e.g., vectors that are close in distance when plotted to a virtual space) (page 6, paragraph [0090]);
for each cluster in the plurality of clusters: determining a cluster centroid (determining a central point of the vector representations and defining the cluster as a sphere of predetermined diameter extending from the centroid) (page 6, paragraph [0090]);
determining a defined number of nearest neighbors to the cluster centroid (The system 200 can perform vector-cluster comparisons by computing distance metrics 109, 111 between the input vector 103 and each semantic cluster 105, 107. For example, to generate the distance metric 109, the system 200 computes a squared Euclidean distance between the input vector 103 and a centroid of the semantic cluster 105) (page 7, paragraph [0099]); and
generating a summary of the cluster to the defined number of the nearest neighbors (In one example, the communication service 204 generates a report including communication data 212, identifications of one or more clusters to which the communication data 212 was matched, and a confidence metric corresponding to the cluster match (for example, a distance metric or a comparison between the distance metric and a predetermined matching threshold). The communication service 204 can flag, highlight, or otherwise identify natural language inputs and subsets thereof for further review by a user) (pages 8 and 9, paragraph [0110]).
Nieborowski et al fails to teach generating a summary of the cluster by applying an abstractive summarization.
Bar Eliyahu et al teaches generating a summary of the cluster by applying an abstractive summarization (abstractive summary models aim to generate summaries that do not necessarily contain exact phrases or sentences from the original text, but instead capture the main ideas and concepts presented in the text) (col. 2, line 65-col. 3, line 1).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski with the teachings of Bar Eliyahu to speed up communication and improving natural language processing by generating an abstractive summarization report which captures just the main ideas in the textual data and requires smaller datasets.
Claim 17 is rejected for the same reason as claim 10.
Claim 18 is rejected for the same reason as claim 11.
Claim 19 is rejected for the same reason as claim 12.
Claims 9, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nieborowski et al and Bar Eliyahu et al fail as applied to claim1 above, and further in view of Jacob et al (US 2024/0362036).
Regarding Claim 9, Nieborowski et al teaches the method, wherein performing the sentence embedding includes converting sentences to fixed sized vectors in a continuous vector space (For example, the system can convert a key phrase text string to a 720-dimension vector) (page 6, paragraph [0090]).
Nieborowski et al and Bar Eliyahu et al fail to teach the method, wherein the fixed sized vectors respectively represent different token meanings and semantic relationships between the different tokens .
Jacob et al teaches the method, herein the fixed sized vectors (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56) (pages 3 and 4, paragraph [0033])respectively represent different token meanings (An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56) (pages 3 and 4, paragraph [0033]) and semantic relationships between the different tokens(The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space) (pages 3 and 4, paragraph [0033]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski and of Bar Eliyahu with the teachings of Jacob to improve natural language processing by providing accurate matching of natural language data.
Regarding Claim 14, Nieborowski et al and Bar Eliyahu et al fail to teach the method, wherein each cluster in the plurality of clusters represents a different topic, and wherein the clustering model is a k-means clustering model.
Jacob et al teaches the method, wherein each cluster in the plurality of clusters represents a different topic, and wherein the clustering model is a k-means clustering model (Additionally, or alternatively, the analytics server may use a clustering algorithm (e.g., K-means or hierarchical clustering) in which similar vectors are clustered together, such that different clusters of tokenized texts that exhibit similar patterns, themes, or contexts are identified) (page 15, paragraph [0106]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski and of Bar Eliyahu with the teachings of Jacob to improve natural language processing by providing accurate matching of natural language data by using K-means clustering.
Claim 16 is rejected for the same reason as claim 9.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Nieborowski et al and Bar Eliyahu et al fail as applied to claim 1 above, and further in view of Haake et al (US 2024/0111883).
Regarding Claim 13, Nieborowski et al and Bar Eliyahu et al fail to teach the method, wherein the similarity threshold is between 80 percent and 90 percent.
Haake et al teaches the method, wherein the similarity threshold is between 80 percent and 90 percent (In the shown example, let each smaller-sized circle (of which there are ten in total) correspond to a cluster of identical role pairs, and each larger circle (of which there are three) correspond to roles with a similarity above a given threshold (e.g., seventy, eighty, or ninety percent similarity or in a range of 70-99 or 80-100 percent or in a range bounded by the example thresholds) (page 6, paragraph [0073]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski and of Bar Eliyahu with the teachings of Haake to improve natural language processing by more accurate matching of natural language data by having a high similarity threshold.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Nieborowski et al and Bar Eliyahu et al fail as applied to claim above, and further in view of Haake et al (US 2024/0111883) and Jacob et al (US 2024/0362036).
Regarding Claim 20, Nieborowski et al, Bar Eliyahu et al fail to teach the computer-readable medium, wherein the similarity threshold is between 80 percent and 90 percent, wherein each cluster in the plurality of clusters represents a different topic, and wherein the clustering model is a k-means clustering model.
Haake et al teaches he computer-readable medium, wherein the similarity threshold is between 80 percent and 90 percent (In the shown example, let each smaller-sized circle (of which there are ten in total) correspond to a cluster of identical role pairs, and each larger circle (of which there are three) correspond to roles with a similarity above a given threshold (e.g., seventy, eighty, or ninety percent similarity or in a range of 70-99 or 80-100 percent or in a range bounded by the example thresholds) (page 6, paragraph [0073]).
Nieborowski et al, Bar Eliyahu et al and Haake et al fail to teach the computer-readable medium, wherein the clustering model is a k-means clustering model.
Jacob et al teaches the computer-readable medium, wherein the clustering model is a k-means clustering model (Additionally, or alternatively, the analytics server may use a clustering algorithm (e.g., K-means or hierarchical clustering) in which similar vectors are clustered together, such that different clusters of tokenized texts that exhibit similar patterns, themes, or contexts are identified) (page 15, paragraph [0106]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Nieborowski and of Bar Eliyahu with the teachings of Haake and Jacob to improve natural language processing by providing accurate matching of natural language data by using K-means clustering and a high similarity threshold.
Allowable Subject Matter
Claims 1-7 would be allowable if the 35 USC 101 rejections above are overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claims 1-7 of the current application teach similar subject matter as the prior art of Nieborowski et al. (US 2023/0315993), Bar-0n (US 2022/0413660), and Liu et al. (US 2021/0281593). The prior art alone or in combination teaches “A system comprising: a processor; and a memory comprising computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: receiving a text data; performing text processing on the text data, the text processing including: converting text in the text data to string format; and applying tokenization to the converted text; performing sentence embedding on each token generated from the tokenization, the sentence embedding including converting sentences to fixed sized vectors in a vector space; generating a plurality of clusters by applying a clustering model to the converted sentences; for each cluster in the plurality of clusters: determining a cluster centroid; determining nearest neighbors to the cluster centroid; determining a similarity score between a number of the nearest neighbors; comparing the similarity score to a similarity threshold; upon the similarity score being less than the similarity threshold, generating a first summary of the cluster by applying abstractive summarization to the defined number of the nearest neighbors” as recited in claim 1. However, the prior art alone or in combination fails to teach “upon the similarity score being greater than the similarity threshold, generating a second summary of the cluster by selecting the nearest neighbor from the nearest neighbors as the second summary” as recited in claim 1.
Claims 2-7 would be allowable for being dependent on an allowable base claim if the 35 USC 101 rejections above are overcome
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhong et al. (US 11,481,448) discloses semantic matching and retrieval of standardized entities.
Choi et al. (US 2021/0201143) discloses classifying category of data.
Attwater et al. (US 2023/0244855) discloses automatic summarization in interlocutor turn-based electronic conversational flow.
Gado et al. (US 2025/0045532) discloses classifying feedback from transcripts.
Kalaichelvan et al. (US 2025/0274465) discloses two-stage anomalous device detection.
Conclusion
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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