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 .
Status of the Claims
The amendment received on 21 January 2026 has been acknowledged and entered.
Claims 1, 10, and 18 have been amended. No new claims have been added.
Claims 1-20 are currently pending.
Response to Amendments and Arguments
Applicant's amendments filed 21 January 2026 with respect to the rejection of claim 1 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Thus, the rejection of claim 1 under 35 U.S.C. 112(b) has been withdrawn.
Applicant's arguments filed 21 January 2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 11-12) that 1. Step 2A, Prong 1 - The Claims Are Not Directed to a Judicial Exception… The Examiner previously asserted that the claims are directed to a mental process, organizing human activity, and/or commercial interactions. As amended, that characterization no longer applies. The focus of the claims is not on human decision-making, contractual relationships, or economic principles, but on a specific computational technique for transforming unstructured and structured data into high- dimensional numeric representations and using those representations in automated model-based inference. The amended claims require generating multi- dimensional numeric embedding vectors using trained machine learning models and neural network architectures. These embedding vectors represent data in high-dimensional vector spaces and are generated through model execution, not through human cognition. The specification explains that embeddings may be generated using techniques such as Word2Vec, GloVe, BERT, or deep learning models such as Siamese networks, and NASH that such embeddings capture semantic relationships and contextual information in ways that go far beyond what can be mentally performed. The computation of similarity relationships between such vectors, using metrics such as cosine similarity or Euclidean distance, further underscores that the claimed operations are machine-only operations that cannot practically be performed in the human mind.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, using trained machine learning models and neural network architectures does not take the claims out of the “Certain methods of organizing human activity” grouping since the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions) and activity that involves multiple people (such as a commercial interaction involving customers/service providers ), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. Secondly, the Examiner has reviewed the specification and determined that the underlying claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment and is 3) is merely using a computer as a tool to perform the concept. Lastly, the claims certainly are related to sales activities or behaviors, and business relations (Commercial Activities) for matching customers and service providers and providing recommended price offers. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 12-13) that although the claims involve determining an incentive price, that determination is not a mental judgment or business rule. Instead, the incentive price is computed by executing a model-based inference that produces a numeric output as a function of similarity relationships between high-dimensional embedding vectors. The pricing determination therefore arises from the internal operation of trained models and vector computations, not from human reasoning or negotiation. USPTO guidance makes clear that processes which cannot practically be performed mentally do not constitute mental processes, even if they involve mathematical relationships. See MPEP §2106.04(a). Likewise, the claims are not directed to organizing human activity or commercial interactions. While the output of the system may be used in a commercial marketplace, the claims themselves are directed to how a computer system processes data using machine learning techniques to generate incentive prices. The Federal Circuit has repeatedly held that claims are not abstract simply because they are used in a commercial environment, where the claimed advance lies in a technological improvement rather than in a business concept. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016). Here, the claimed advance lies in the use of embedding-based inference to enable automated, scalable matching and pricing, not in the commercial context in which the system operates.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, using trained machine learning models and neural network architectures does not take the claims out of the “Certain methods of organizing human activity” grouping since the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions) and activity that involves multiple people (such as a commercial interaction involving customers/service providers ), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. Secondly, the Examiner has reviewed the specification and determined that the underlying claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment and is 3) is merely using a computer as a tool to perform the concept. Thirdly, the claims certainly are related to sales activities or behaviors, and business relations (Commercial Activities) for matching customers and service providers and providing recommended price offers. Lastly, unlike Enfish, the claims do not specifically improve computer functionality; they merely use computer functionality. Applicant’s claims appear to apply preexisting trained models to determine an incentive price. Further, there is not an improvement to machine learning technology. Therefore, the Examiner suggests providing more details or the mechanisms whereby the system/computer/technology is improved by providing an incentive price in order to move the claims towards patent eligibility.
Applicant argues (in REMARKS, pages 13-14) that 2. Step 2A, Prong 2 - The Claims Are Integrated Into a Practical Application, even if the claims were considered to involve an abstract idea, they are integrated into a practical application. As amended, the claims recite a specific technical implementation that improves the functioning of a computer-based service matching platform.
The specification explains that embeddings reduce dimensionality and make it computationally feasible to compare millions of previous jobs with a current job request, something that would be infeasible using conventional rule- based or manual techniques. By transforming job descriptions and historical job data into high-dimensional numeric embedding vectors and using similarity-based inference to compute incentive prices, the claimed system enables real- time, automated computation that improves system scalability, efficiency, and accuracy. This is a concrete technological improvement in how computer systems process and analyze data in large-scale service marketplaces.
The claims do not merely invoke a generic processor as a tool to perform an abstract idea. Instead, they recite the use of trained machine learning models and neural network architectures in a specific sequence of operations that produce a technical result, namely, a model-generated incentive price derived from vector similarity computations. This constitutes integration into a practical application under MPEP §2106.05(a) and aligns with Federal Circuit precedent such as McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), where claims were held eligible because they used specific computational techniques to automate tasks in a way that improved computer functionality.
In response to Applicant’s arguments, the Examiner respectfully disagrees for reasons previously discussed. Further, the embeddings are recited at a high level of generality and are used as intended. Secondly, unlike McRO, in which the claims were directed to an improvement in computer animation and thus did not recite a concept similar to previously identified abstract ideas, Applicant’s claims are directed to the use of embedding vectors for providing an incentive price for a customer to offer to a service provider which does appear to provide a technical improvement to a technical problem or automates a process to produce a specific, tangible, and superior "output" (like realistic animation). Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 14-15) that 3. Step 2B - The Claims Recite Significantly More Than Any Judicial Exception. Even assuming, for the sake of argument, that the claims were directed to a judicial exception, the amended claims recite additional elements that amount to significantly more. The requirement of trained machine learning models, neural network-generated embeddings, high-dimensional vector similarity computation, and model-based inference reflects a non-conventional arrangement of computing components and techniques. These elements are not generic computer functions, but rather specific technical mechanisms that enable the claimed improvement in automated service matching and pricing. The specification makes clear that the claimed system is a special-purpose computing system configured to perform machine learning-based inference using historical data, statistical weights, and neural network architectures. This goes well beyond merely instructing a generic computer to apply an abstract idea and instead reflects a technological solution rooted in computer science and machine learning. As in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), the claimed invention addresses a problem arising in computer-implemented systems and provides a solution that is necessarily rooted in computer technology.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that unlike DDR, Applicant’s claims do not provide a solution that is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. Instead, the claims is merely claiming that concept performed on a generic computer system and in a computer environment and is merely using the computer as a tool to perform the concept of providing a first incentive price by use of a model and embedding. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, page 15) that the dependent claims further limit the independent claims by specifying additional technical features, such as additional embedding inputs, category and sub-category processing, and iterative incentive price generation based on model outputs. Because the independent claims are patent- eligible, the dependent claims are likewise patent-eligible. For the foregoing reasons, as amended, claims 1-20 are not directed to an abstract idea, are integrated into a practical application, and recite significantly more than any alleged judicial exception. Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.
In response to Applicant’s arguments, the Examiner respectfully disagrees for reason stated above regarding the rejection of claim 1.
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 recites an abstract idea without significantly more.
Step 1
Claims 1-9 are directed to a method (i.e., a process); Claims 10-17 are directed to a system (i.e., a machine); and Claims 18-20 are directed to non-transitory computer-readable storage mediums (i.e., a manufacture). Therefore, Claims 1-6 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1, 10, and 18 substantially recite:
applying [ ] one or more trained models to generate a multi-dimensional customer embedding vector for a customer of a content provider, the customer embedding vector being generated by processing based on a job description for a category of service currently requested by the customer;
applying [ ] one or more trained models to generate a multi-dimensional numeric service provider embedding vector for a service provider, the service provider embedding vector being generated by processing data associated with at least one previous job accepted by the service provider through the content provider;
determining [ ] a first incentive price for the category of service currently requested by the customer by executing a model-based inference that computes incentive value as a function of a similarity relationship between the customer embedding vector and the service provider embedding vector; and
providing to the customer [ ] the first incentive price generated by the model-based inference as a recommendation for the customer to offer through the content provider for an available service provider to perform the category of service currently requested by the customer. The aforementioned limitations may be interpreted as at least as a “Mental Process” (concepts performed in the human mind) which includes observations, evaluations, judgments, and opinions and/or “Managing Personal Behavior or Relationships or Interactions Between People” which includes social activities, teaching, and following rules or instructions and/or “Commercial Interactions” which includes agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. That is, nothing in the claim elements preclude the steps from practically being performed by the human mind (determining); managing personal behavior or relationships or interactions between people (applying, applying, determining, and providing); and commercial interactions (applying, applying, determining, and providing).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements (e.g. “a computer system” and “at least one neural network model”); claim 10 recites the additional element (e.g. “a system,” “at least one processor,” “a memory,” “instructions,” and “at least one neural network model”); and claim 18 recites the additional element (e.g. “a non-transitory computer- readable medium,” “instructions,” “at least one processor,” “a computing system,” and “at least one neural network model”)– using the “system to perform the “applying,” “applying,” “determining,” and “providing” in claims 1, 10, and 18. The “processor/system” in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “applying,” “applying,” “determining,” and “providing” in claims 1, 10, and 18) such that it amounts no more than mere instructions to “apply” the exception using a generic computer component. That is, the aforementioned limitations merely invoke the generic components as a tool to perform the abstract idea, e.g. see MPEP 2106.05(f).
Further, in regards to the “processor and/or system” the “providing” limitation in claim 1, 10, and 18 is just mere data gathering, and also are characterized as transmitting or receiving data over a network and insignificant post-solution activity, and are also recited at a high level or generality, and merely automates the receiving and obtaining steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Independent claims 1, 10, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the “computer system“ in claim 1; the additional element of using the “system,” “at least one processor,” “memory,” and “instructions” in claim 10; and the additional element of using the “non-transitory computer- readable medium,” “instructions,” “at least one processor,” and “computing system” in claim 18 to perform the “applying,” “applying,” “determining,” and “providing” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are ineligible.
As per dependent claims 2, 11, and 19, the recitation, “determining a second incentive price for the category of service currently requested by the customer when the first incentive price is not accepted, and the second incentive price is based on a higher amount over the first incentive price” is further directed to a method of organizing human activity and/or a mental process as described in claims 1, 10, and 18,, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 3, 12, and 20, the recitation, “to the customer, the second incentive price as a recommendation for the customer to offer to the available service provider through the content provider to perform the category of service currently requested by the customer” is further directed to a method of organizing human activity and/or a mental process as described in claims 1, 10, and 18,, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 4-9 and 13-17, the limitations merely narrow the previously recited abstract idea limitations. Dependent claims 4 and 13 recite the category of service comprises a main category and a sub-category. Dependent claims 5 and 14 recites the customer embedding is generated at least in part on text included in the job description of a desired date and a time range for a job start. Dependents claim 6 and 15 recite the customer embedding is generated at least in part on text included in the job description of a zip code and a service address. Dependent claims 7 and 16 recite the service provider embedding is generated at least in part on text of a price included in the at least one previous job accepted by the service provider. Dependent claims 8 and 17 recite the service provider embedding is generated at least in part on text of a time range for a job start included in the at least one previous job accepted by the service provider. Dependent claim 9 recites the service provider embedding is generated at least in part on text of a zip code and a service address included in the at least one previous job accepted by the service provider. For the reasons described above with respect to claims 4-9 and 13-17, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent Claims 2-9, 11-17, and 19-20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-9, 11-17, and 19-20, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Prior Art Discussion
1) Anderson et al. (US PG Pub. 2021/0216979 A1) discloses identifying, soliciting, selecting and scheduling service providers by deriving intrinsic data using natural language processing techniques to extract keywords and phrases and other attributes that can be used for matching service providers to a service request; and upon matching service providers to a service request, the system sends bid solicitations to matched service providers, who may then respond with requests for additional information or bids.
2) Sharp et al. (US PG Pub. 20240289813 A1) discloses interpreter services lifecycle applications an environment which includes a matching engine that can perform natural language processing to enable the matching engine to process and understand service descriptions and provider profiles at a semantic level; and word embedding models, such as, Word2Vec or GloVe can convert textual information into numerical vectors, which facilitate the matching of services with similar requirements or provider expertise based on the meaning of the text.
3) Markowitz, Dale, “Meet AI’s multitool: Vector embeddings”, March 23, 2022, cloud.google.com, 17 pages discloses that, in machine learning, an embedding is a way of representing data as points in n-dimensional space so that similar data points cluster together and discloses a list of what can be built with embeddings.
However, while the prior art cited above teaches some aspects and features of claims 1-20, the Examiner emphasizes the unique combination of features as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
The features are:
determining, by the computer system, a first incentive price for the category of service currently requested by the customer by executing a model-based inference that computes a numeric incentive value as a function of a similarity relationship between the customer embedding vector and the service provide embedding; and
providing to the customer, by the computer system, the first incentive price generated by the model-based inference as a recommendation for the customer to offer through the content provider for an available service provider to perform the category of service currently requested by the customer.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Mokhtarani, Shabnam, “Embeddings in Machine Learning: Everything You Need to Know“, August 26, 2021, featureform.com, 18 pages.
2) Kisilevich, Slava, “ Learning product similarity in e-commerce using a supervised approach: A practical solution to finding similar products using deep learning. A product-centric approach”, April 27, 2022, towardsdatascience.com, 21 pages.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDA A. NELSON whose telephone number is (571)272-7076. The examiner can normally be reached Monday-Friday, 10:00am - 6:30pm.
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, Shannon Campbell can be reached at 571-272-5587. 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.
/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628