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 .
Status of Claims
This communication is a first office action non-final rejection on the merits. Claim(s) 1-20, as filed on 04/30/2025, are currently pending and have been fully considered below.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and thus do not satisfy the criteria for subject matter eligibility.
Step 1
Claim(s) 1, 12, 17 fall(s) in two of the four statutory categories of invention.
Step 2A Prong One: Yes
The limitations of claim(s) 1, 12, 17 recite(s):
A method comprising:
generating, , a primary embedding, wherein the primary embedding is associated with a generic line item corresponding to a service job to be performed by a tenant;
generating, a plurality of secondary embeddings, wherein each of the plurality of secondary embeddings is associated with a particular tenant- specific line item from a plurality of tenant-specific line items;
based on comparing the primary embedding to each of the plurality of secondary embeddings, determining a subset of tenant-specific line items that corresponds to the generic line item from the plurality of tenant-specific line items; and
in response to receiving, associated with the tenant, input data indicating that the service job is to be performed by the tenant for a customer, causing
The limitations of claims 1, 12, 17 recite concepts of reimbursement decision for a drug, which falls into the grouping of Certain Methods of Organizing Human Activity. More specifically, the claim language recites concepts that generating data (A, B), comparing data (C), transmitting data (D), and thus are considered commercial practice known in the inventory business.
Claims 1-20 recite an abstract idea.
Step 2A Prong Two: No
Claim(s) 1, 12, 17 additional elements are:
Claims 1 and 12: “using the machine learning model” “embedding”
Claims 1 and 17: “at a user device”
Claims 12: “a user device associated with a tenant; and at least one computing device configured to”;
Claim 17: “A non-transitory computer-readable medium storing instructions that, when executed, cause:”
The claimed additional elements that perform limitations A, B are claimed at a high level of generality and are considered nothing more than data analyzes using a existent technology (i.e.; Machine learning) without the recitation of an improvement, and thus are considered generality linking the use of the judicial exception to a particular technological environment and/or field of use; The claimed additional elements that perform limitation C is claimed at a high level of generality and are considered nothing more than data comparison, and thus are mere instructions to implement an abstract idea on a computer; The claimed additional elements that perform limitation D is claimed at a high level of generality and are considered nothing more than data transmitting and displaying, and thus are mere instructions to implement an abstract idea on a computer; when view in combination, the additional elements merely describe how to generally “apply” the abstract idea in a generic or general-purpose computer, and generality links the use of the judicial exception to a particular technological environment or field of use, and thus do not integrate the abstract idea into a practical application, and claim(s) 1, 12, 17 are directed to the judicial exception.
Claims 1-20 are directed toward an abstract idea.
Step 2B: No
As discussed with respect to Step 2A Prong Two, the additional elements in the claims generally linking the use of the judicial exception to a particular technological environment or field of use (i.e., computer technology) such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The same analysis applies here in 2B, i.e., does not recite any additional element or combination of elements that amounts to significantly more than the selected exception.
Further, considered as an ordered combination, the additional elements of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas of infrastructure management to collect data, receive data, and generate reports that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
Further, the Office have found that receiving and transmitting data over the network is not enough to be patent-eligible, see MPEP 2106.05(d), that gathering data is not enough is not enough to be patent-eligible, see MPEP2106.05(g). The processing data is not enough is not enough to be patent-eligible, 2106.05(f), 2106.05(g).
Even when the steps are considered in combination, did not amount to an inventive concept.
As for dependent claims 2-11, 13-16, 18-20, the claims merely recite limitations that further narrow the abstract idea recited on claims 1, 12, 17, and thus fail to amount significantly more.
Therefore, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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(s) 1-2, 5-6, 10, 12-13, 15, 17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Binshtock et al. (US20230143975A1, hereinafter Binshtock) in view of Biryukov et al. (US 20250328734 A1, hereinafter Biryukov).
Regarding claim(s), 1, 12, 17, Binshtock discloses:
A method comprising: generating, using a machine learning model, a embedding, wherein the embedding is associated with a generic line item; generating, using the machine learning model, the plurality of embeddings, wherein each of the plurality of embeddings is associated with a particular tenant- specific line item from a plurality of tenant-specific line; (para. 24 ‘Search functions can search the price book comprising all available service items to identify the desired product or service that the user desires”; see Figure 1 para. 25 “the service item for replacing an existing return air filter grille has been identified, and added to the estimate. The smart recommendations are then presented at the bottom of the user interface. In the depicted example, four recommended service items are presented: Seal Ductwork, Installation of an EZ-Trap Drain, Installation of a Programmable Digital Thermostat, and Replacement of an Air Filter component. Each of the recommendations are services commonly associated with the replacement of an existing return air filter grille”, para. 31; Figures 1-10;)
based on comparing the embedding to each of the plurality of secondary embedding, determining a subset of tenant-specific line items that corresponds to the generic line item from the plurality of tenant-specific line items; and in response to receiving, at a user device associated with the tenant, input data indicating that the service job is to be performed by the tenant for a customer, causing display of the subset of tenant-specific line items that corresponds to the generic line item. (para. 38 “non-neural networks can be utilized instead of or in addition to the above examples, in order to generate recommended service items…an embedding vector can be generated for each service items. Service items can be deemed to be related to each other when differences between corresponding vectors are below a threshold amount” para. 21 “ The estimate 100 can be presented via an application or display on a computing device, such as a mobile computing device, tablet, smart phone, or other general computing device associated with a display.”; “The estimate display can additionally provide a description of the product, specifications, and other information”; para. 39 “generate an estimate comprising one or more service items based on selections at a user interface 610” para. 22 “the service item, a replacement of an air filter grille can be further modified with customizations specific to the proposed job or service being quoted”, para. 21 for the customer; Figures 1-10;
Binshtock does not disclose a primary embedding and secondary embeddings;
Biryukov discloses: [0061] The computing server 110 identifies 330 a set of similar transactions using the first embedding by comparing the first embedding representing the transaction record to second embeddings representing historical transactions; Figure 4;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Binshtock to include the above limitations as taught by Biryukov, in order to avoid false positive cases when comparing strings, see Biryukov para. 2.
Regarding claim(s) 2, 13, Binshtock does not disclose: wherein generating the primary embedding comprises converting text associated with the generic line item into a primary vector, and wherein generating the plurality of secondary embeddings comprises converting text associated with each of the plurality of tenant-specific line items into a secondary vector.
Biryukov discloses: [0061] Para. 60 “The embedding model encodes the text of the transaction record as a vector in a latent space, or “an embedding.” And para. 70 machine learning techniques are based on vectors;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Binshtock to include the above limitations as taught by Biryukov, in order to avoid false positive cases when comparing strings, see Biryukov para. 2.
Regarding claim(s) 5, 6, 15, 19, Binshtock discloses:
wherein comparing the embedding to each of the plurality of embeddings comprises determining a similarity between the embedding and each of the plurality of embeddings wherein determining the subset of the tenant-specific line items that corresponds to the generic line item comprises determining a subset of the plurality of embeddings for which the similarity satisfies a threshold. para. 38 “para. 38 “non-neural networks can be utilized instead of or in addition to the above examples, in order to generate recommended service items…an embedding vector can be generated for each service items. Service items can be deemed to be related to each other when differences between corresponding vectors are below a threshold amount”, see Figures 1-10 – recommended items are displayed;”, see Figures 1-10;
Binshtock does not disclose a primary embedding and secondary embeddings;
Biryukov discloses: [0061] The computing server 110 identifies 330 a set of similar transactions using the first embedding by comparing the first embedding representing the transaction record to second embeddings representing historical transactions; Figure 4;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Binshtock to include the above limitations as taught by Biryukov, in order to avoid false positive cases when comparing strings, see Biryukov para. 2.
Regarding claim(s) 10, Binshtock does not disclose wherein the machine learning model is a large language model (LLM).
Biryukov discloses: [00041] open-source large language model;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Binshtock to include the above limitations as taught by Biryukov, in order to avoid false positive cases when comparing strings, see Biryukov para. 2.
Claim(s) 3-4, 14, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Binshtock and Biryukov combination as applied to claims 1, 12, 17, and further in view of Burke et al. (US 20190005019 A1, hereinafter Burke).
Regarding claims 3-4, 14, 18, the combination does not disclose: “wherein the at least one computing device is further configured to: cause storage of the plurality of secondary embeddings in an embedding database further comprising: updating the stored plurality of secondary embeddings in response to determining that the plurality of tenant-specific line items has been modified.”
Burke discloses: [00026] “the word embedding modeler 124 may create, read, update, and/or delete the word embeddings 111; and Figure 1 see Item 110;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the combination to include the above limitations as taught by Burke, in order to have a word embedding model increasingly sophisticated and capable of generating rich associations between vectors, see Burke para. 26.
Claim(s) 9, 16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Binshtock and Biryukov combination as applied to claims 1, 12, 17, and further in view of Herron et al. (US 12548389 B1, hereinafter Herron).
Regarding claim(s) 9, 16, 20, the combination, specifically Binshtock discloses:
further comprising: receiving, at the user device, a user selection of a first tenant-specific line item from the subset of the tenant-specific line items; and (Figures 1-10 – recommended items are displayed; [0023] When at least one service item 120 is selected and added to the estimate, the present invention can provide one or more recommended service items 110 to be added to the estimate. The recommended items 110 can easily be added to the estimate via a selection button 140 In embodiments, when an additional service item is added to the estimate, the present technology can update its service item recommendations based on the addition of the new item..);
The combination does not disclose generating an invoice for the service job to be performed by the tenant for the customer based at least in part on the first tenant-specific line item.
Herron discloses: 9:10-50 Upon initiating repairs based on the estimate file 102, a repair order 104 may be generated, such as from the estimating software 132, where the repair order 104 comprises an electronic file that details the repair services required for repair of the vehicle 22. An electronic invoice file 106 is also generated, and in accordance with aspects of the present invention repair service documentation data files 108
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the combination to include the above limitations as taught by Herron, in order to confirming and validating what services were performed, see Herron 9:10-50.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Binshtock and Biryukov combination as applied to claim 1, and further in view of Lado et al. (US 11544555 B1, hereinafter Lado).
Regarding claim(s) 11, the combination does not disclose: wherein the generic line item comprises a generic service or a generic material, and wherein each of the plurality of tenant-specific line items comprises a tenant-specific service or a tenant-specific material.
Lador discloses: Table 3 repair car; and repair car are common from the list of the business on table 3; on Table 1 all those companies performed those specific service under invoice text; see cols 5-12;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the combination to include the above limitations as taught by Lado, in order to identify the relationships and corresponding suggestions from invoices for new products that a business may offer, see Lado 1:5-20.
Allowable Subject Matter
According claim(s) 7-8 are include allowable subject matter over art and would be allowed if applicant overcomes the 35 USC 101 rejection and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VANESSA DELIGI whose telephone number is (571)272-0503. The examiner can normally be reached on Monday-Friday 07:30AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian (Ryan) Zeender can be reached on (571) 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VANESSA DELIGI/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/ Supervisory Patent Examiner, Art Unit 3627
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