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 . This action is responsive to the application filed on 02/23/2024. Claims 1-20 are presented in the case. Claims 1, 11 and 20 are independent claims.
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.
Claims 2-4 and 12-14 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 pre-AIA the applicant regards as the invention.
Claims 2 and 12 recite the limitation "the second model". There is insufficient antecedent basis for this limitation in the claim.
Claims 3 and 13 recite the limitation " the output". There is insufficient antecedent basis for this limitation in the claim.
Claims 4 and 14 recite the limitation " the output". There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 are directed to a system, claims 11-19 are directed to a method and claim 20 is directed to a medium. Therefore, the claims are eligible under Step 1 for being directed to a machine, a process, and a manufacture respectively.
Independent claims 1, 11 and 20:
Step 2A Prong 1:
Claims recite:
identify a plurality of machine learning (ML) models for a plurality of domains, each of the plurality of ML models trained using at least a plurality of texts on a respective domain of the plurality of domains, each respective domain of the plurality of domains covers a plurality of topics - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing, evaluating data and identifying models based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
identify a classification ML model trained to classify the plurality of ML models according to the plurality of topics of the plurality of domains - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing, evaluating data and identifying model based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
select a first ML model of the plurality of ML models trained on the domain associated with the topic using at least a first portion of the query corresponding to a domain of the plurality of domains input into the classification ML model - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting models based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
generate, using at least a second portion of the query corresponding to a topic of the domain input into the first ML model, a response to the query - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating a response to the query based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
A system, comprising:
one or more processors coupled with memory; A non-transitory computer-readable media having processor readable instructions, such that, when executed, the processor readable instructions cause at least one processor to - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
receive a query on a topic - the step recited at a high level of generality amount to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
provide, for display, the response - the step recited at a high level of generality amount to mere data outputting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
A system, comprising:
one or more processors coupled with memory; A non-transitory computer-readable media having processor readable instructions, such that, when executed, the processor readable instructions cause at least one processor to - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
receive a query on a topic - the steps recited at a high level of generality, and amounts to mere data gathering which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
provide, for display, the response - the steps recited at a high level of generality, and amounts to mere data outputting which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 2 and 12:
Step 2A Prong 1:
Claims recite:
parse the query into the first portion indicative of the domain and the second portion indicative of the topic - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
select the second model using at least the first portion of the query input into the classification ML model, the first portion indicative of the domain of payroll services to employees of an enterprise - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; and
generate the response to the query using at least the second portion of the query indicative of the topic corresponding to a payroll service of the payroll services within a geographic area - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating a response to the query based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claims 3 and 13:
Step 2A Prong 1:
Claims recite:
generate, using at least the first portion of the query input into a processing ML model trained using at least the plurality of texts on queries to produce a plurality of outputs indicative of the plurality of topics concerning employees of one or more enterprises, the output indicative of the topic - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; and
select the first ML model using at least the output of the processing ML model as an input into the classification ML model - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claims 4 and 14:
Step 2A Prong 1:
Claims recite:
identify, based at least on the query input into a first processing ML model trained using a textual content corresponding to a plurality of tones, a tone of a text of the query - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing, evaluating data and identifying a tone of a text of the query based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
update, based at least on the tone of the text and the response input into a second processing ML model trained on a plurality of responses for the plurality of tones, the response according to the tone - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing, evaluating data and updating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
provide, for display, the response updated by the second processing ML model - the step recited at a high level of generality amount to mere data outputting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
provide, for display, the response updated by the second processing ML model - the steps recited at a high level of generality, and amounts to mere data outputting which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 5 and 15:
Step 2A Prong 1:
Claims recite:
select, using at least the first portion of the query and the classification ML model, a second ML model of the plurality of ML models corresponding to a second domain associated with the topic - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; and
generate, using at least the second portion of the query input into the second ML model, a second response to the query based at least on the topic - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating a response to the query based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claims 6 and 16:
Step 2A Prong 1:
Claims recite:
generate a first score corresponding to a relation between the query and the domain and a second score corresponding to a second relation between the query and the second domain - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
rank the response and the second response according to the first score and the second score - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and ranking data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
provide the response for display responsive to the rank of the response - the step recited at a high level of generality amount to mere data outputting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
provide the response for display responsive to the rank of the response - the steps recited at a high level of generality, and amounts to mere data outputting which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 7 and 17:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the domain of the plurality of domains corresponds to a rule for an employee of an enterprise in one or more geographical areas of a plurality of geographical areas and the topic corresponds to a geographical area of the one or more geographical areas - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the domain of the plurality of domains corresponds to a rule for an employee of an enterprise in one or more geographical areas of a plurality of geographical areas and the topic corresponds to a geographical area of the one or more geographical areas - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 8 and 18:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more rules on taxation of employees of one or more enterprises within one or more geographical areas - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more rules on taxation of employees of one or more enterprises within one or more geographical areas - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 9 and 19:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more laws or one or more rules on wages for employees of one or more enterprises within one or more geographical areas - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more laws or one or more rules on wages for employees of one or more enterprises within one or more geographical areas - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 10:
Step 2A Prong 1: The claim recites the abstract ideas of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more rules on benefits for employees of one or more enterprises within one or more geographical areas - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the domain of the plurality of domains corresponds to at least one or more rules on benefits for employees of one or more enterprises within one or more geographical areas - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Claim Rejections - 35 USC § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-6, 11, 13-16 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sen et al. (hereinafter Sen), US 20250252319 A1.
Regarding independent claim 1, Sen teaches a system, comprising:
one or more processors coupled with memory to ([0058] FIG. 5 is a block diagram of an example of an electronic device 500 used for entering commands or queries, executing functions relating to generating, training, combining and using LLMs, and displaying answers provided by LLMs in a user interface, in accordance with some embodiments of the disclosure. In an embodiment, the equipment device 500, is the same equipment device 402 of FIG. 4. The equipment device 500 may receive content and data via input/output (I/O) path 502. The I/O path 502 may provide audio content (e.g., broadcast programming, on-demand programming, internet content, content available over a local area network (LAN) or wide area network (WAN), and/or other content) and data to control circuitry 504, which includes processing circuitry 506 and a storage 508; [0062] Memory may be an electronic storage device provided as the storage 508 that is part of the control circuitry 504):
receive a query on a topic (Fig. 9; [0101] In some embodiments, a question is received at block 910. In some embodiments, the question may be a simple question with a single ask or query that may be inputted by a user in an input area on a user interface; [0102] In other embodiments, the question may be a multipart or complex question that may require multiple answers or answers that are based on multiple factors);
identify a plurality of machine learning (ML) models for a plurality of domains, each of the plurality of ML models trained using at least a plurality of texts on a respective domain of the plurality of domains, each respective domain of the plurality of domains covers a plurality of topics ([0068] FIG. 6 is flowchart of an example of a process for obtaining enterprise data and generating and training enterprise LLMs; [0070] In some embodiments, at block 610, the control circuitry, such as the control circuitry 428 and/or 420 of system 400 in FIG. 4, may access enterprise data to obtain and analyze the enterprise data and use it as training data for an ELLM; [0075] At block 630, the control circuitry 428 and/or 420 may extract data from all the servers, such as email server, department servers, executable applications, document libraries, databases, employee files stored on local or shared drives, and any storage used by the enterprise or an employee of the enterprise for storing data for which authorization is provided; [0076] At block 640, the data accessed may be classified into categories; [0079] At block 670, once the data has been classified and curated for quality, such data may be used by the control circuitry 428 and/or 420 to generate K number of ELLMs … The generated ELLMs may then be trained at block 680 using the classified and quality curated data; [0160] FIG. 15 is a block diagram of an example of selecting enterprise LLMs from different departments in a company based on their contextual relationship to the input question. In some embodiments, a question 1510 may be received by the ELLM selector 1520 … Since an enterprise may include a plurality of ELLMs, where each ELLM may be associated with a specific department of the enterprise, the ELLM selector may determine based on the question 1510 which department specific ELLM is most relevant to the question and select that ELLM to process the question; [0163] In some embodiments, as depicted in FIG. 16, a general ELLM may include further nested ELLM that are more specific within the general topic);
identify a classification ML model trained to classify the plurality of ML models according to the plurality of topics of the plurality of domains ([0038] At block 320, in some embodiments, the control circuitry, such as the control circuitry 428 and/or 420 of system 400 in FIG. 4, may generate K number of ELLMs using the training data generated. In doing so, the control circuitry 428 and/or 420 may use classified data that is curated for its quality to generate a plurality of ELLMs. Such ELLMs may be ELLMs that provide different tiers of access to content where such access is authorized based on the job level or title of an employee, as depicted in FIG. 14, ELLMs that are department specific, as depicted in FIG. 15, nested ELLMs that are further sub-categorized under a main category, as depicted in FIG. 16);
select a first ML model of the plurality of ML models trained on the domain associated with the topic using at least a first portion of the query corresponding to a domain of the plurality of domains input into the classification ML model ([0105] At block 920, the control circuitry 428 and/or 420 may analyze the content and/or context of the question as well as the user that asked the question. With respect to content and/or context of the question, the results obtained from analyzing the content and/or context of the question may be used to determine which ELLM to use; [0110] In some embodiments, one or more factors may be analyzed in the narrowing process. One such factor used may be the content and context of the question received at block 910. In some embodiment, an enterprise may have k number of ELLMs, each ELLM relating to a specific function of the enterprise or a specific department, such as engineering, human resources etc. If results from analyzing the content and context of the question at blocks 910-920 indicate that the question related to finance, then control circuitry 428 and/or 420 may. narrow the k number of ELLMs to only those ELLM that relate to finance. Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS; [0111] In yet another embodiment, even within a department of an enterprise or within an ELLM that has been trained with data relating to a specific function, there may be nested or further specific ELLMs that relate to different subtopics or categories within the larger category of the ELLM);
generate, using at least a second portion of the query corresponding to a topic of the domain input into the first ML model, a response to the query ([0102] In other embodiments, the question may be a multipart or complex question that may require multiple answers or answers that are based on multiple factors; [0109] At block 930, also depicted at block 1020 of FIG. 10, the control circuitry 428 and/or 420 may narrow the number of ELLMs from k ELLMs to n ELLMs, where k is a larger number than n; [0110] In some embodiments, one or more factors may be analyzed in the narrowing process. One such factor used may be the content and context of the question received at block 910 … Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS; [0122] At block 940, once the k number of ELLMs are narrowed to n number of ELLMs, and the k number of LLMs are narrowed to n number of LLMs, the control circuitry 428 and/or 420, in some embodiments, may determine a sequence of use for the narrowed set of ELLMs and LLMs; [0125] The sequence referred to in blocks 940 and 950 may be part of a strategy deployed by the control circuitry 428 and/or 420. Once the sequence has been executed, based on the strategy determined, the control circuitry 428 and/or 420 at block 960 may determine whether the strategy needs to be revised. For example, in one embodiment, an original strategy determined may be to use a first set of ELLMs and/or LLMs, from the narrowed set of n ELLMs and n LLMs, in a particular sequence, or simultaneously, and then use the results from the first set of ELLMs and/or LLMs to feed it into a second set of first set of ELLMs and/or LLMs, from the n ELLMs and n LLMs, to obtain an answer; [0127] At block 970, the control circuitry 428 and/or 420 may obtain answers from all the ELLMs and/or LLMs as part of the sequence and strategy deployed in blocks 950-965; [0128] At block 980, the control circuitry 428 and/or 420 may blend the answers obtained at block 970; [0130] At block 985, the control circuitry 428 and/or 420 may input the blended answer into an ensemble model, such as the ensemble model 1060 in FIG. 10); and
provide, for display, the response ([0131] At block 990, the control circuitry 428 and/or 420 may obtain a golden answer from the ensemble model and display it to the user from whom the question was received).
Regarding dependent claim 3, Sen teaches all the limitations as set forth in the rejection of claim 5 that is incorporated. Sen further teaches comprising the one or more processors to:
generate, using at least the first portion of the query input into a processing ML model trained using at least the plurality of texts on queries to produce a plurality of outputs indicative of the plurality of topics concerning employees of one or more enterprises, the output indicative of the topic ([0087] The data extraction module 810 once invoked, may extract data from all enterprise resources and data storage locations, such as enterprise servers, such as email server, department servers, executable applications, document libraries, databases, employee files stored on local or shared drives, and any storage used by the enterprise or an employee of the enterprise for storing data. The data extraction module may use techniques such as extract, transform, and load (ETL) techniques and extract, load, transform (ELT) techniques to extract date from the enterprise resources and storage locations. It may also use crawlers, scraping software tools, API integration, data mining, database querying, text pattern matching and other types of large data extraction techniques to extract data from all enterprise resources and data storage locations used by the enterprise, including from other existing ELLMs; [0105] At block 920, the control circuitry 428 and/or 420 may analyze the content and/or context of the question as well as the user that asked the question. With respect to content and/or context of the question, the results obtained from analyzing the content and/or context of the question may be used to determine which ELLM to use; [0110] In some embodiments, one or more factors may be analyzed in the narrowing process. One such factor used may be the content and context of the question received at block 910. In some embodiment, an enterprise may have k number of ELLMs, each ELLM relating to a specific function of the enterprise or a specific department, such as engineering, human resources etc. If results from analyzing the content and context of the question at blocks 910-920 indicate that the question related to finance, then control circuitry 428 and/or 420 may. narrow the k number of ELLMs to only those ELLM that relate to finance. Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS); and
select the first ML model using at least the output of the processing ML model as an input into the classification ML model ([0089] The data classification module 820 may review each piece of data extracted from by the extraction module 810 and determine its context and applicability. For example, if a document includes employment details, or is a document that is an employee handbook, employee vacation policy, employee review, employee compensation, etc., then analyzing the content and the context the data classification module 820 may determine that it should be classified as a human resources document. As such, a human resources or HR tag may be added to that piece of data. The data classification module 820 may further determine that even within HR, employee compensation should be classified as its own category since that is used specifically for hiring and retention and may be used by a specific group within HR; [0109] At block 930, also depicted at block 1020 of FIG. 10, the control circuitry 428 and/or 420 may narrow the number of ELLMs from k ELLMs to n ELLMs, where k is a larger number than n).
Regarding dependent claim 4, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen further teaches
identify, based at least on the query input into a first processing ML model trained using a textual content corresponding to a plurality of tones, a tone of a text of the query ([0106] Analyzing the question may, in some embodiments, involve using natural language processing (NLP) techniques. The system may use the NLP technique to obtain an understanding of the entered text or voice note. For example, if the question was entered by using voice recognition methods, such as voice to text, NLP techniques may used to understand the content and context of the question. NLP techniques may also be used in conjunction with other tools, such as sentiment analysis tools, to capture the sentiment and mood of the user when asking the question);
update, based at least on the tone of the text and the response input into a second processing ML model trained on a plurality of responses for the plurality of tones, the response according to the tone ([0106] a frustrated or angry customer service user may be may be provided with different answer than a user that is experiencing the problem first time and just needs a solution); and
provide, for display, the response updated by the second processing ML model ([0106] a frustrated or angry customer service user may be may be provided with different answer than a user that is experiencing the problem first time and just needs a solution).
Regarding dependent claim 5, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen further teaches comprising the one or more processors to:
select, using at least the first portion of the query and the classification ML model, a second ML model of the plurality of ML models corresponding to a second domain associated with the topic ([0109] At block 930, also depicted at block 1020 of FIG. 10, the control circuitry 428 and/or 420 may narrow the number of ELLMs from k ELLMs to n ELLMs, where k is a larger number than n; [0110] In some embodiments, one or more factors may be analyzed in the narrowing process. One such factor used may be the content and context of the question received at block 910 … Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS; [0122] At block 940, once the k number of ELLMs are narrowed to n number of ELLMs, and the k number of LLMs are narrowed to n number of LLMs, the control circuitry 428 and/or 420, in some embodiments, may determine a sequence of use for the narrowed set of ELLMs and LLMs; [0125] The sequence referred to in blocks 940 and 950 may be part of a strategy deployed by the control circuitry 428 and/or 420. Once the sequence has been executed, based on the strategy determined, the control circuitry 428 and/or 420 at block 960 may determine whether the strategy needs to be revised. For example, in one embodiment, an original strategy determined may be to use a first set of ELLMs and/or LLMs, from the narrowed set of n ELLMs and n LLMs, in a particular sequence, or simultaneously, and then use the results from the first set of ELLMs and/or LLMs to feed it into a second set of first set of ELLMs and/or LLMs, from the n ELLMs and n LLMs, to obtain an answer; [0127] At block 970, the control circuitry 428 and/or 420 may obtain answers from all the ELLMs and/or LLMs as part of the sequence and strategy deployed in blocks 950-965; [0128] At block 980, the control circuitry 428 and/or 420 may blend the answers obtained at block 970); and
generate, using at least the second portion of the query input into the second ML model, a second response to the query based at least on the topic ([0130] At block 985, the control circuitry 428 and/or 420 may input the blended answer into an ensemble model, such as the ensemble model 1060 in FIG. 10; [0131] At block 990, the control circuitry 428 and/or 420 may obtain a golden answer from the ensemble model and display it to the user from whom the question was received).
Regarding dependent claim 6, Sen teaches all the limitations as set forth in the rejection of claim 5 that is incorporated. Sen further teaches comprising the one or more processors to:
generate a first score corresponding to a relation between the query and the domain and a second score corresponding to a second relation between the query and the second domain ([0118] In some embodiments, the system may analyze a cost and accuracy trade-off. In other words, since higher accuracy may require more computational processing power thereby incurring more costs, the system may determine whether there is a benefit to incur the higher costs based on the type of question asked and the importance of the question. Some examples of the cost, accuracy, and combination used to narrow the number of LLMs (or ELLMs) are depicted in FIGS. 20A-C. In FIG. 20A, LLMs 1-4 may provide a costs basis for answering the same query inputted into the LLM. For example, to provide a response to the same query, LLM2 may have the lowest cost basis of 2.8 while LLM4 may have the highest cost basis of 3.9. The system, or the user, may narrow the number of LLMs (or ELLMs) based on the costs and select, for example, LLM2 since it has the lowest costs. The system, or the user, may also select the two (or other number) lowest LLMs (or ELLMs) based on costs. Similarly, in FIG. 20B, LLM7 may provide the highest accuracy and as such may be selected. In other embodiments, the top two, three, or other number of highest accuracy producing);
rank the response and the second response according to the first score and the second score ([0119] LLMs may be selected. FIG. 20C depicts selection of an accuracy level based on the importance of the question to the user. A question that is very importance, such as question 2 (Q2) having an importance level 8 associated with it, on a scale of 10, may be suitable for an LLM that provides 93% accuracy. On the other hand, a question of lower importance, such as Q1 may be suitable for an LLM that provides 62% accuracy. Although references to tables and LLMs are made in FIGS. 20A-C, the embodiments include ELLMs and other formats of data, such as in charts, graphs, histograms, and other formats may also be used); and
provide the response for display responsive to the rank of the response ([0131] At block 990, the control circuitry 428 and/or 420 may obtain a golden answer from the ensemble model and display it to the user from whom the question was received).
Regarding independent claim 11, it is a method claim corresponding to the system of claim 1. Therefore, it is rejected for the same reason as claim 1 above.
Regarding dependent claim 13, it is a method claim corresponding to the system of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 14, it is a method claim corresponding to the system of claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 15, it is a method claim corresponding to the system of claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding dependent claim 16, it is a method claim corresponding to the system of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding independent claim 20, it is a media claim corresponding to the system of claim 1. Therefore, it is rejected for the same reason as claim 1 above.
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 2, 7-10, 12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sen as applied in claims 1 and 11, in view of Thakur et al. (hereinafter Thakur), US 20250028715 A1.
Regarding dependent claim 2, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen does not explicitly teach comprising the one or more processors to:
parse the query into the first portion indicative of the domain and the second portion indicative of the topic;
select the second model using at least the first portion of the query input into the classification ML model, the first portion indicative of the domain of payroll services to employees of an enterprise; and
generate the response to the query using at least the second portion of the query indicative of the topic corresponding to a payroll service of the payroll services within a geographic area.
However, in the same field of endeavor, Thakur teaches
parse the query into the first portion indicative of the domain and the second portion indicative of the topic (Fig. 3; [0023] Query 302 may include a data unit 304 and any additional associated information 306. In some embodiments, data unit 304 may include information that is processed by machine learning models as part of workflow 300 whereas associated information 306 may be any additional information that is not processed by the models but may be used (e.g., by QPE 138) after the machine learning models have completed processing (e.g., classification) of data unit 304. For example, a data unit 304 may include a title of a document (e.g., “Job descriptions of Company A”) and associated information 306 may include job titles, job descriptions, and/or other information). In another example, associated information 306 may be referenced in data unit 304. For example, data unit 304 may include a request to collect information about email security policy adopted by public universities in a given state and compare the policies with the current policy of the client attached as associated information 306. In some instances, query 302 may include data unit 304 and does not include any associated information 306. For example, data unit 304 may include a request to “find a range of salaries of radiologists in rural areas of New England” with no associated information 306 provided; [0024] Data unit 304 may undergo tokenization 310 that segments data unit 304 into one or more tokens 320. Tokens 320 may refer to any portion of data unit 304 having individual semantic meaning. In the last example, tokens 320 may include four tokens: “salaries” (or “range of salaries”), “radiologists,” “rural areas,” and “New England”; Fig. 4; [0037] At block 410, method 400 may include identifying, by a processing device, a query that includes a data unit (e.g., query 302 and data unit 304, as depicted in FIG. 3 ). At block 420, method 400 may continue with representing, by the processing device, the data unit via one or more tokens (e.g., by performing tokenization 310 to obtain tokens 320));
select the second model using at least the first portion of the query input into the classification ML model, the first portion indicative of the domain of payroll services to employees of an enterprise ([0038] At block 430, method 400 may include processing, by the processing device, the one or more tokens using a plurality of machine learning models (MLMs) to identify one or more clusters of a plurality of clusters. Each of the one or more identified clusters may be associated with at least one token of the one or more tokens of the data unit; [0002] Human resource management may include maintaining job descriptions, managing employee salaries and benefits, tracking job-related activities and relations of employees, supporting employee recruiting, monitoring employee satisfaction, keeping abreast of changes in the relevant industries and geographic areas, and/or the like; [0010] a client may be interested in learning about best business practices of human resource management, salary and benefits ranges of various jobs, on-site safety, and/or the like; [0016] Server machine 130 may include a query standardization engine (QSE) 132 trained to identify and group data units having different lexical form but the same and/or similar semantic meaning, as disclosed herein. QSE 132 may include SM 134 trained to identify associations of tokens in data units with anchors 112 of various clusters 111 based on statistics of co-appearance of respective tokens and anchors 112 in data units. QSE 132 may further include LM 136 trained to identify associations of tokens with various clusters 111 using natural language processing techniques); and
generate the response to the query using at least the second portion of the query indicative of the topic corresponding to a payroll service of the payroll services within a geographic area ([0016] Server 130 may further include a query processing engine (QPE) 138 to facilitate retrieval of data during processing of new search queries. For example, a client may submit, via one of client machines 140, a request for information, e.g., a request to find statistics about salary and benefits of nurse practitioners in a particular geographic area. QSE 132 may process tokens in the client request and identify most likely associations of the tokens with one or more clusters 111, thus determining semantic meanings of the tokens. QPE 138 may then retrieve stored information related to the determined semantic meanings, e.g., to multiple forms in which the same or substantially the same semantic meanings are expressed in different words. Upon retrieving stored information, QPE 138 may eliminate false hits, combine positive hits, generate relevant statistics, compile a report, and/or perform any other operations that may be expected by the client.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of tokenizing the received query and processing the tokens using a plurality of machine learning models (MLMs) to identify cluster(s) associated with at least one token as suggested in Thakur into Sen’s system because both of these systems are addressing using machine learning for comprehensive query processing. This modification would have been motivated by the desire to provide systems and techniques capable of using machine learning for automated data standardization and efficient data query processing (Thakur, [0011]).
Regarding dependent claim 7, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen does not explicitly teach wherein the domain of the plurality of domains corresponds to a rule for an employee of an enterprise in one or more geographical areas of a plurality of geographical areas and the topic corresponds to a geographical area of the one or more geographical areas.
However, in the same field of endeavor, Thakur teaches wherein the domain of the plurality of domains corresponds to a rule for an employee of an enterprise in one or more geographical areas of a plurality of geographical areas and the topic corresponds to a geographical area of the one or more geographical areas ([0013] FIG. 1 illustrates a high-level component diagram of an example system architecture 100, in accordance with one or more aspects of the present disclosure. The system architecture 100 (also referred to as “system” herein) includes a data store 110, one or more data pods (DPs) 120-1, 120-2 . . . 120-N, a server machine 130, one or more client machines 140, and/or other components connected to a network 150; [0014] In some embodiments, DPs 120-j may store any suitable raw and/or processed data units that may be collected from any number of clients, e.g., businesses, public and private foundations, government agencies, non-profit organizations, institutions, associations, charities, partnerships, and/or the like. Different DPs 120-j may be serving (e.g., collecting information from) different geographic areas, states, industries, businesses of different types and sizes, and/or the like. In some embodiments, DPs 120-j may store information that includes job titles, job descriptions, salaries, benefits, employment policies, laws and government regulations, listing of services provided by clients to customers, inventories of goods, and/or any other suitable public and/or private data. DPs 120-j may store information together with various structures that tag, organize, and index the data. Data store 110 may store various data and metadata used and/or generated to facilitate processing of information collected in DPs 120-j. In some embodiments, data store 110 may store token clusters 111 (also referred to as simply clusters throughout this disclosure) that group various data units collected in DPs 120-j by semantic meaning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of organizing processed data collected from different geographic areas, states, industries, businesses of different types and sizes into clusters that group various data units by semantic meaning as suggested in Thakur into Sen’s system because both of these systems are addressing using machine learning for comprehensive query processing. This modification would have been motivated by the desire to facilitate various database queries to return comprehensive results that include multiple relevant items of information (Thakur, [0011]).
Regarding dependent claim 8, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen does not explicitly teach wherein the domain of the plurality of domains corresponds to at least one or more rules on taxation of employees of one or more enterprises within one or more geographical areas.
However, in the same field of endeavor, Thakur teaches wherein the domain of the plurality of domains corresponds to at least one or more rules on taxation of employees of one or more enterprises within one or more geographical areas ([0013] FIG. 1 illustrates a high-level component diagram of an example system architecture 100, in accordance with one or more aspects of the present disclosure. The system architecture 100 (also referred to as “system” herein) includes a data store 110, one or more data pods (DPs) 120-1, 120-2 . . . 120-N, a server machine 130, one or more client machines 140, and/or other components connected to a network 150; [0014] In some embodiments, DPs 120-j may store any suitable raw and/or processed data units that may be collected from any number of clients, e.g., businesses, public and private foundations, government agencies, non-profit organizations, institutions, associations, charities, partnerships, and/or the like. Different DPs 120-j may be serving (e.g., collecting information from) different geographic areas, states, industries, businesses of different types and sizes, and/or the like. In some embodiments, DPs 120-j may store information that includes job titles, job descriptions, salaries, benefits, employment policies, laws and government regulations, listing of services provided by clients to customers, inventories of goods, and/or any other suitable public and/or private data. DPs 120-j may store information together with various structures that tag, organize, and index the data. Data store 110 may store various data and metadata used and/or generated to facilitate processing of information collected in DPs 120-j. In some embodiments, data store 110 may store token clusters 111 (also referred to as simply clusters throughout this disclosure) that group various data units collected in DPs 120-j by semantic meaning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of organizing processed data collected from different geographic areas, states, industries, businesses of different types and sizes into clusters that group various data units by semantic meaning as suggested in Thakur into Sen’s system because both of these systems are addressing using machine learning for comprehensive query processing. This modification would have been motivated by the desire to facilitate various database queries to return comprehensive results that include multiple relevant items of information (Thakur, [0011]).
Regarding dependent claim 9, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen does not explicitly teach wherein the domain of the plurality of domains corresponds to at least one or more laws or one or more rules on wages for employees of one or more enterprises within one or more geographical areas.
However, in the same field of endeavor, Thakur teaches wherein the domain of the plurality of domains corresponds to at least one or more laws or one or more rules on wages for employees of one or more enterprises within one or more geographical areas ([0013] FIG. 1 illustrates a high-level component diagram of an example system architecture 100, in accordance with one or more aspects of the present disclosure. The system architecture 100 (also referred to as “system” herein) includes a data store 110, one or more data pods (DPs) 120-1, 120-2 . . . 120-N, a server machine 130, one or more client machines 140, and/or other components connected to a network 150; [0014] In some embodiments, DPs 120-j may store any suitable raw and/or processed data units that may be collected from any number of clients, e.g., businesses, public and private foundations, government agencies, non-profit organizations, institutions, associations, charities, partnerships, and/or the like. Different DPs 120-j may be serving (e.g., collecting information from) different geographic areas, states, industries, businesses of different types and sizes, and/or the like. In some embodiments, DPs 120-j may store information that includes job titles, job descriptions, salaries, benefits, employment policies, laws and government regulations, listing of services provided by clients to customers, inventories of goods, and/or any other suitable public and/or private data. DPs 120-j may store information together with various structures that tag, organize, and index the data. Data store 110 may store various data and metadata used and/or generated to facilitate processing of information collected in DPs 120-j. In some embodiments, data store 110 may store token clusters 111 (also referred to as simply clusters throughout this disclosure) that group various data units collected in DPs 120-j by semantic meaning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of organizing processed data collected from different geographic areas, states, industries, businesses of different types and sizes into clusters that group various data units by semantic meaning as suggested in Thakur into Sen’s system because both of these systems are addressing using machine learning for comprehensive query processing. This modification would have been motivated by the desire to facilitate various database queries to return comprehensive results that include multiple relevant items of information (Thakur, [0011]).
Regarding dependent claim 10, Sen teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Sen does not explicitly teach wherein the domain of the plurality of domains corresponds to at least one or more rules on benefits for employees of one or more enterprises within one or more geographical areas.
However, in the same field of endeavor, Thakur teaches wherein the domain of the plurality of domains corresponds to at least one or more rules on benefits for employees of one or more enterprises within one or more geographical areas ([0013] FIG. 1 illustrates a high-level component diagram of an example system architecture 100, in accordance with one or more aspects of the present disclosure. The system architecture 100 (also referred to as “system” herein) includes a data store 110, one or more data pods (DPs) 120-1, 120-2 . . . 120-N, a server machine 130, one or more client machines 140, and/or other components connected to a network 150; [0014] In some embodiments, DPs 120-j may store any suitable raw and/or processed data units that may be collected from any number of clients, e.g., businesses, public and private foundations, government agencies, non-profit organizations, institutions, associations, charities, partnerships, and/or the like. Different DPs 120-j may be serving (e.g., collecting information from) different geographic areas, states, industries, businesses of different types and sizes, and/or the like. In some embodiments, DPs 120-j may store information that includes job titles, job descriptions, salaries, benefits, employment policies, laws and government regulations, listing of services provided by clients to customers, inventories of goods, and/or any other suitable public and/or private data. DPs 120-j may store information together with various structures that tag, organize, and index the data. Data store 110 may store various data and metadata used and/or generated to facilitate processing of information collected in DPs 120-j. In some embodiments, data store 110 may store token clusters 111 (also referred to as simply clusters throughout this disclosure) that group various data units collected in DPs 120-j by semantic meaning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of organizing processed data collected from different geographic areas, states, industries, businesses of different types and sizes into clusters that group various data units by semantic meaning as suggested in Thakur into Sen’s system because both of these systems are addressing using machine learning for comprehensive query processing. This modification would have been motivated by the desire to facilitate various database queries to return comprehensive results that include multiple relevant items of information (Thakur, [0011]).
Regarding dependent claim 12, it is a method claim corresponding to the system of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 17, it is a method claim corresponding to the system of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Regarding dependent claim 18, it is a method claim corresponding to the system of claim 8. Therefore, it is rejected for the same reason as claim 8 above.
Regarding dependent claim 19, it is a method claim corresponding to the system of claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Brown et al. (US 20190042988 A1) discloses an artificial intelligence (AI) agent system providing adaptive features (e.g., adjusts behavior over time to improve how AI agent system reacts/responds to users), stateful features (e.g., past conversations with users are remembered and are part of context of AI agent system when interacting with user), scalable features (e.g., flexible deployment options for the AI agent system allow for on-client deployment, cloud based deployment, on-enterprise deployment, and hybrid deployment), and/or security features (e.g., product security may be architected into basic structure of AI agent system to allow full enterprise control and visibility over usage, rights control, and authentication methods).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER WELCH can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMY P HOANG/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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