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 .
Detailed Action
Claim Construction
The third limitation in claim 8 recites “mapping, with at least one processor, the text character data to a tree-based data structure in memory locations of the memory based on the one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents,” and the fourth limitation in claim 18 recites “map the text character data to a tree-based data structure in memory locations of the memory based on one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents.” Examiner notes that, in each limitation, the language “that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents” contains the indefinite language “allows for naïve clustering” and “can represent all possible semantic variation” which do not positively recite performing naïve clustering or representing all possible semantic variations. Thus Examiner does not give this language patentable weight in these claims.
Objections
Claim 5 is objected to because of the following informality: this claim recites “the frequency value” but, if this value one of a list of values that may be determined in claim 4 so, if it is not determined then the term lacks antecedent basis in claim 5.
Claim 8 is objected to because of the following informality: the second limitation recites “receiving, with at least one processor, a text dataset in the form of a second electronic document file, the electronic document file including text character data,” and “the electronic document file” lacks antecedent basis.
Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claim 1 recites loading and executing a machine learning model stored on the first storage device, wherein the text dataset is provided as input to the machine learning model; generating an inference for the text character data based on analyzing one or more features of the text character data; generating plural text data vectors for the text character data based on at least one of the plural child nodes and the root node associated with the text character data, wherein each of the plural text data vectors corresponds to a memory location in the memory; and generating at least one display output based on the plural text data vectors for the text character data. Loading and executing a machine learning is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Generating an inference for text character data, generating plural text data vectors for text character data, and generating a display output are each recited broadly and are mental processes accomplishable in the human mind or on paper. Claim 1 recites additional elements of receive a text dataset including text character data, an input step and insignificant extra-solution activity; map the text character data to a tree-based data structure in memory locations of the memory based on one or more dimensions of the text character data, wherein the tree-based data structure includes a recursive network including a root node and plural child nodes associated with the root node, wherein the tree-based data structure contains a lower layer of child nodes associated with the root node, and wherein the text character data is mapped to the lower layer of child nodes in the memory, which is storing data and extra-solution activity; and display the display output on the at least one display device, which is an output step and also insignificant extra-solution activity. Claim 1 recites a memory, a first storage device, at least one display device, and a processor configured with program code, which are each generic components of a computer system. Examiner notes specification paragraph 0003 discusses how documents may be modified and paragraph 0004 discusses how said document modifications may be completely lost, causing substantial, unnecessary rework, and how many documents are stored that have only slight differences from other documents causing an increase in storage space. Paragraph 0004 also says “there is a need to consolidate changes and/or variations of multiple electronic documents stored in a database in order to reduce storage space and capture and/or track changes that have been made to reduce processing time when reviewing, creating, and managing new documents.” Examiner found further details of the invention’s improvements in paragraphs 0006-0007, 0045, and 0155, but the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, receiving a text dataset and displaying output are recited broadly and amount to receiving and sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which are each routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Mapping text character data to a tree-based data structure in memory is storing data, which is also routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. The memory, a first storage device, at least one display device, and processor configured with program code are still each generic components of a computer system. Thus the claim does not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Independent claim 8 recites generating, with at least one processor, a second set of text data vectors for the text character data based on the mapping of the text character data to the tree-based data structure in the memory; comparing, with at least one processor, the second set of text data vectors for the text character data to the first set of text data vectors corresponding to the first electronic document file, wherein differences between the second set of text data vectors and the first set of text data vectors are stored as delta text data vectors; and detecting, with at least one processor, at least one semantic difference between the first electronic document file and the second electronic document file based on the delta text data vectors. Generating a set of text data vectors, comparing text data vectors, and detecting a difference between files are each recited broadly and are mental processes accomplishable in the human mind or on paper. This claim recites additional elements of storing, with at least one processor, a first set of text data vectors in memory corresponding to a first electronic document file, which is insignificant extra-solution activity; receiving, with at least one processor, a text dataset in the form of a second electronic document file, the electronic document file including text character data, which is an input step and also insignificant extra-solution activity; and mapping, with at least one processor, the text character data to a tree-based data structure in memory locations of the memory based on the one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents, which is also a storing step and insignificant extra-solution activity. Examiner notes specification paragraph 0003 discusses how documents may be modified and paragraph 0004 discusses how said document modifications may be completely lost, causing substantial, unnecessary rework, and how many documents are stored that have only slight differences from other documents causing an increase in storage space. Paragraph 0004 also says “there is a need to consolidate changes and/or variations of multiple electronic documents stored in a database in order to reduce storage space and capture and/or track changes that have been made to reduce processing time when reviewing, creating, and managing new documents.” Examiner found further details of the invention’s improvements in paragraphs 0006-0007, 0045, and 0155, but the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, receiving a text dataset is recited broadly and amounts to receiving data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Storing text data vectors and mapping text character data to a data structure in memory are each also routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Thus the claim does not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Independent claim 18 recites classifying the text character data based on a classification output of the text character data generated by the machine learning model; generating plural text data vectors for the text character, wherein the plural text data vectors represent the mapping of the text character data to the tree-based data structure; and generating a display output based on the plural text data vectors for the text character data. Classifying text character data is recited broadly and is a mental process accomplishable in the human mind or on paper, and generating output from a machine learning model is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Generating text data vectors and generating a display output are each recited broadly and is also a mental process accomplishable in the human mind or on paper. This claim recites additional elements of receiving text character data, and input step and insignificant extra-solution activity; inputting the text character data to a machine learning model, also an input step and insignificant extra-solution activity; mapping the text character data to a tree-based data structure in memory locations of the memory based on one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents, a storing step and insignificant extra-solution activity; and displaying the display output on the at least one display device, an output step and also insignificant extra-solution activity. This claim recites a non-transitory computer-readable medium which is a generic component of a computer system. Examiner notes specification paragraph 0003 discusses how documents may be modified and paragraph 0004 discusses how said document modifications may be completely lost, causing substantial, unnecessary rework, and how many documents are stored that have only slight differences from other documents causing an increase in storage space. Paragraph 0004 also says “there is a need to consolidate changes and/or variations of multiple electronic documents stored in a database in order to reduce storage space and capture and/or track changes that have been made to reduce processing time when reviewing, creating, and managing new documents.” Examiner found further details of the invention’s improvements in paragraphs 0006-0007, 0045, and 0155, but the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input and output steps are recited broadly and amounts to receiving data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II, and the storing step is also routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. The non-transitory computer-readable medium is still a generic component of a computer system. Thus the claim does not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites wherein each child node in the memory is associated with a label based on the child node and each parent node that is associated with the child node, and associating nodes in memory is recited broadly and a mental process accomplishable in the human mind or on paper. Claim 3 recites wherein, when mapping the text character data to the tree-based data structure in memory locations of the memory based on the one or more dimensions of the text character data, the program code will cause the processor to map the text character data to one or more higher-level child nodes associated with the lower- level child nodes and the root node, and mapping data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claims 4 and 20 each recites determine a frequency value, rarity value, or conditional rule-based value of the plural text data vectors for the text character data in the memory, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and display the frequency value, rarity value, or conditional rule-based value of the plural text data vectors for the text character data as the display output on the at least one display device, and displaying data is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II.
Claims 5 and 15 each recites wherein the display output includes the text character data displayed as text characters including a coloration of the text characters corresponding to the frequency value, wherein the coloration of the text characters is based on a frequency value range of 0% to 100%, and displaying data is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 6 recites determine a score for the text dataset based on the plural text data vectors, wherein the score is determined using a semantic polarity of the plural text data vectors for the text character data in the memory, and determining a score is evaluating and a mental process; and display the score for the text dataset as a table on the at least one display device, and displaying data is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claims 7 and 17 each recites receive plural text datasets, each text dataset including text character data, and receiving data is recited broadly and amounts to receiving data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; store, in the memory, plural scores of the plural text datasets, each text dataset resulting in the plural data vectors for the text character data in the memory, and storing data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; determine an average score for plural text datasets received by the processor based on the plural scores stored in the memory, and determining a score is evaluating and a mental process; and display the average score with the score for a single text dataset on the at least one display device, and displaying data is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II.
Claim 9 recites generating, with the at least one processor, a display output based on the delta text data vectors, the second text data vectors, and the first text data vectors, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and displaying, with a display device, the display output, and displaying data is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claims 10 and 19 each recites wherein the tree-based data structure includes a recursive network of root nodes and plural child nodes associated with the root nodes, wherein a first portion of the plural child nodes are lower-level child nodes and a second portion of the plural child nodes are higher-level child nodes, and wherein the text character data is mapped to the lower-level child nodes in the memory, and a data structure and mapping text character data to a memory is storing it, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 11 recites executing a trained machine learning model, which is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628); inputting the text character data to the trained machine learning model, and inputting data to a machine learning model is recited broadly and is a mental process accomplishable in the human mind or on paper; and generating, with the trained machine learning model, one or more classifications including encoding a constituent and/or syntactic structure of electronic documents for the text character data based on analyzing one or more features of the text character data in the electronic document file, which is applying a machine learning model and is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628).
Claim 12 recites wherein the one or more classifications include any one of a document classification, a section classification, a sentence classification, a phrase classification, and a token classification, and applying a machine learning model and is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claim 13 recites executing plural trained machine learning models, which is applying the machine learning models and is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628); inputting the text character data to the plural trained machine learning models, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and generating, with the plural trained machine learning models, plural classifications for the text character data based on analyzing one or more features of the text character data in the second electronic document file, which is applying the machine learning models and is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628); wherein the plural classifications include any one of a document classification, a section classification, a sentence classification, a phrase classification, and a token classification, which is applying the machine learning models and is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628).
Claim 14 recites determining a frequency value of text data vectors of the second set of text data vectors for the text character data in the memory, and determining is evaluating and a mental process; and displaying the frequency value of the text data vectors of the second set of text data vectors as a display output on the at least one display device, which is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 16 recites determining a score for the second electronic document file based on the second set of text data vectors, wherein the score is determined using the frequency value of the text data vectors of the second set of text data vectors, and determining is evaluating and a mental process; and displaying the score for the text dataset in a table format on the at least one display device, which is recited broadly and amounts to sending data over a network per specification paragraphs 0022, 0037, 0080-0081, and figure 3 network 310, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II
Rejections under 35 U.S.C. 102
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.
Claims 1, 3, and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al (US 20240176962), hereafter Jain.
With respect to claim 1, Jain teaches:
receive a text dataset including text character data (paragraphs 0046 captured corpus of data from utterances as text, also paragraph 0045);
load and execute a machine learning model stored on the first storage device, wherein the text dataset is provided as input to the machine learning model (paragraph 0048 input utterances into machine learning model to get utterance trees);
generate an inference for the text character data based on analyzing one or more features of the text character data (paragraph 0006 generate inference of utterance data based on analysis by a model, also paragraph 0007);
map the text character data to a tree-based data structure in memory locations of the memory based on one or more dimensions of the text character data, wherein the tree-based data structure includes a recursive network including a root node and plural child nodes associated with the root node, wherein the tree-based data structure contains a lower layer of child nodes associated with the root node, and wherein the text character data is mapped to the lower layer of child nodes in the memory (paragraph 0045 create utterance tree from text utterance data, root and child nodes, example in paragraph 0091 figure 6 of an utterance tree created using words or phrases (dimensions) of the utterance, paragraph 0125 figure 15 dimensions used to determine similarity of words in vectors, example in paragraph 0126);
generate plural text data vectors for the text character data based on at least one of the plural child nodes and the root node associated with the text character data, wherein each of the plural text data vectors corresponds to a memory location in the memory (paragraph 0045 vectors of txt data in nodes of tree so based on root node);
generate at least one display output based on the plural text data vectors for the text character data (paragraph 0155 generate output based on word vectors, paragraph 0122 generate agent utterances as output using word vectors); and
display the display output on the at least one display device (paragraph 0122 display agent utterances).
With respect to claim 3, Jain teaches wherein, when mapping the text character data to the tree-based data structure in memory locations of the memory based on the one or more dimensions of the text character data, the program code will cause the processor to map the text character data to one or more higher-level child nodes associated with the lower- level child nodes and the root node (paragraph 0098 figure 9, text character data mapped t parent nodes used to extract meaning representation from the utterance tree).
With respect to claim 8, Jain teaches:
storing, with at least one processor, a first set of text data vectors in memory corresponding to a first electronic document file (paragraph 0045 word vectors stored with utterance document, for example email);
receiving, with at least one processor, a text dataset in the form of a second electronic document file, the electronic document file including text character data (paragraph 0045 receive second utterances having text character data);
mapping, with at least one processor, the text character data to a tree-based data structure in memory locations of the memory based on the one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents (paragraph 0045 create utterance tree from text utterance data, root and child nodes, example in paragraph 0091 figure 6 of an utterance tree created using words or phrases (dimensions) of the utterance, paragraph 0125 figure 15 dimensions used to determine similarity of words in vectors, example in paragraph 0126);
generating, with at least one processor, a second set of text data vectors for the text character data based on the mapping of the text character data to the tree-based data structure in the memory (paragraph 0045 vectors of txt data in nodes of tree so based on root node);
comparing, with at least one processor, the second set of text data vectors for the text character data to the first set of text data vectors corresponding to the first electronic document file, wherein differences between the second set of text data vectors and the first set of text data vectors are stored as delta text data vectors (paragraph 0050 comparing vectors, determining vector distances); and
detecting, with at least one processor, at least one semantic difference between the first electronic document file and the second electronic document file based on the delta text data vectors (paragraph 0074 determining distance between two utterances based on vectors).
Rejections under 35 U.S.C. 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.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Ammar et al (US 20200364451), hereafter Ammar.
With respect to claim 18, Jain teaches:
receive text character data (paragraphs 0046 captured corpus of data from utterances as text, also paragraph 0045);
input the text character data to a machine learning model (paragraph 0048 input utterances into machine learning model to get utterance trees);
map the text character data to a tree-based data structure in memory locations of the memory based on one or more dimensions of the text character data that allows for naive clustering of similar documents and can represent all possible semantic variation within a corpus of documents (paragraph 0045 create utterance tree from text utterance data, root and child nodes, example in paragraph 0091 figure 6 of an utterance tree created using words or phrases (dimensions) of the utterance, paragraph 0125 figure 15 dimensions used to determine similarity of words in vectors, example in paragraph 0126);
generate plural text data vectors for the text character, wherein the plural text data vectors represent the mapping of the text character data to the tree-based data structure (paragraph 0045 vectors of text data in nodes of tree so based on root node);
generate a display output based on the plural text data vectors for the text character data (paragraph 0155 generate output based on word vectors, paragraph 0122 generate agent utterances as output using word vectors); and
display the display output on the at least one display device (paragraph 0122 display agent utterances).
Jain does not teach classify the text character data based on a classification output of the text character data generated by the machine learning model. Ammar teaches this in classifying text blocks in vectors, categorizing them from output (labels) (paragraphs 0040, 0069). It would have been obvious to have combined the classification function in Ammar with the techniques for storing text character sets in trees in Jain to give more information to a user about the text when displayed.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Caroli et al (US 20190317939), hereafter Caroli.
With respect to claim 2, all the limitations in claim 1 are addressed by Jain above. Jain does not teach wherein each child node in the memory is associated with a label based on the child node and each parent node that is associated with the child node. In the same field of endeavor of storing textual data in tree structures, Caroli teaches this in applying an indexing scheme using labels to children and parent nodes in a hierarchical data structure (paragraph 0013) wherein the indexing scheme is used to show relationships between nodes (paragraph 0043). It would have been obvious to have combined this use of labels for nodes in Caroli with the use of a tree storage structure in Jain to distinguish nodes in the utterance tree when correcting errors and updating components of the NLU framework (paragraph 0049).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRUCE M MOSER/Primary Examiner, Art Unit 2154 1/26/26