DETAILED ACTION
Status of Claims
1. This is a Final office action in response to communication received on January 23, 2026. Claims 1-20 are pending and examined herein.
Claim Rejections - 35 USC § 101
2. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-9 are a method; claims 10-18 are a non-transitory CRM; and claim 19-20 are a system. Thus, each claim 1-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of comparing vector embeddings which constitute a vector space which are a mathematical representation of description/text/word of treatments (e.g. incentive, a coupon, or a marketing campaign) to ascertain similarity between known and new treatments exceeds a predetermined value or threshold to target new content (which incorporates new treatment e.g. text/word/description) to audience that responded positively to known treatments in the past and based on interactions of the audience retraining/updating the model which is certain methods of organizing human activity and mathematical concepts.
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
The phrase "Mathematical concepts" applies to mathematical relationships, mathematical formulas or equations, mathematical calculations. Further, see MPEP 2106.04(a)(2) I. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least are (as per claim 1) a trained machine-learned embedding model, finetuning the trained machine-learned embedding model, applying the trained machine-learned embedding model (to known and new treatments to compare similarity exceeding a threshold), and transmitting to client devices (of a target subset users that are targeted with new treatment, wherein the targeted subset of users are identified based on performance of known treatments) for display; (as per claim 10 in addition to claim 1 additional elements note) a non-transitory computer-readable medium, having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps; (as per claim 19 in addition to claim 1 additional elements note) a computing system, comprising: one or more processors; and a non-transitory computer-readable medium, having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps. Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements comprising machine learning model are being utilized as tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1A, 1B, 2, 4, and their associated disclosure. The processor executing the "apply it" instruction is further connected to one or more device merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Receiving and/or displaying data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes the similarity between known and new treatment and compares it with a threshold to select known treatment whose performance is evaluated by testing it on a plurality of users. Further, based on the performance of the known treatment on the plurality of users, a target subset of users is identified to be targeted with the new treatment. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is targeting content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide marketing content, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of comparing vector embeddings which constitute a vector space which are a mathematical representation of description/text/word of treatments (e.g. incentive, a coupon, or a marketing campaign) to ascertain similarity between known and new treatments exceeds a predetermined value or threshold to target new content (which incorporates new treatment e.g. text/word/description) to audience that responded positively to known treatments in the past (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of comparing vector embeddings which constitute a vector space which are a mathematical representation of description/text/word of treatments (e.g. incentive, a coupon, or a marketing campaign) to ascertain similarity between known and new treatments exceeds a predetermined value or threshold to target new content (which incorporates new treatment e.g. text/word/description) to audience that responded positively to known treatments in the past and based on interactions of the audience retraining/updating the model - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here user's data is received and based on analysis targeted promotions are to be transmitted over a network]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution treatments/promotions are communicated or displayed to user on an interface].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
1. The Examiner provides citation to one or more of the court decisions as noting the well-understood, routine, conventional nature of the additional element(s) as follows:
i. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims." - [similarly here an algorithm determines similarity between embedded vectors that represent text converted to numerical value such that similarity between new and old content can be ascertained];
iii. Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 [similarly here an algorithm determines similarity between embedded vectors that represent text converted to numerical value such that similarity between new and old content can be ascertained and appropriate well performing content can be selected to target to users].
2. The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."; (ix) Pub. No.: US2021/0192460A1 “[0064] The machine-learned model embedding service 215 is implemented to generate and train the machine-learned model to map embeddings representing job opportunity content items within a vector space. In one embodiment, the machine-learned model may be a regression model, such as a linear regression model, where input into the model includes features of a job opportunity content item. The output from the model is a representative embedding of the job opportunity content item based on the features received. One technique for implementing the machine-learned model is by using a neural network, such as Word2vec, to produce embeddings for the job opportunity content items based on descriptive features in the job opportunity description. Word2vec is a commercially available deep learning model that implements word embedding configured to generate vector representations of words that capture the context of the word, semantic and syntactics properties of the word, and relations to other words.”; (x) Pub. No.: US 20240152544 A1 see [0047] "In some cases, the resulting input-based attribution 120 may be combined with the attribution of the output 118 Ot which is generated from the embedding 134 (Et) using the input text t using a transformer T. At an output-level, the embeddings ECi may be compared to the training data 108." [0055] "A unique identifier (id) 216 may be assigned to each content item 204 associated with individual creators. A unique id 216(N) may be associated with each of the content items 204 associated with the creator 102(N). For example, the unique id 216(N) may be associated with each of the content items 204 using a deep learning generative model (e.g., Dreambooth or similar) used to fine-tune text-to-image models. The caption extractor 206 may be used to create a caption 208 for each content item 204 if one or more if the content items 204 do not have an associated caption 205 or to supplement the caption 205."; [0103] "At 902, a machine learning algorithm (e.g., software code) may be created by one or more software designers. For example, the generative AI 112 of FIGS. 1 and 3 may be created by software designers. At 904, the machine learning algorithm may be trained (e.g., fine-tuned) using pre-classified training data 906. For example, the training data 906 may have been pre-classified by humans, by machine learning, or a combination of both."; (xi) Pub. No.: US2025/0078453 [1303] "Subsequent to training (e.g., training above an accuracy threshold) and/or fine-tuning the text generation machine learning model 4514, the model manager 4512 can use the text generation machine learning model 4514 in combination with the image generation machine learning model 4516 to generate high scoring images (e.g., based on high scoring prompts generated by the text generation machine learning model 4514)."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) For Withdrawal Of Prior Art
3. Claims 1-4, 7-13, 16-20 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Xu et al. (Pub. No.: US 2021/0192460) referred to hereinafter as Xu, in view of Wu et al. (Pub. No.: US 2020/0177942) referred to hereinafter as Wu.
Claims 5-6 and 14-15 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Xu, in view of Wu and Lamba et al. (Pub. No.: US 2022/0366295) referred to hereinafter as Lamba.
The Examiner also discovered the following which pertain generally to the machine learning aspects:
- Pub. No.: US 20240152544 A1 see [0047] "In some cases, the resulting input-based attribution 120 may be combined with the attribution of the output 118 Ot which is generated from the embedding 134 (Et) using the input text t using a transformer T. At an output-level, the embeddings ECi may be compared to the training data 108." [0055] "A unique identifier (id) 216 may be assigned to each content item 204 associated with individual creators. A unique id 216(N) may be associated with each of the content items 204 associated with the creator 102(N). For example, the unique id 216(N) may be associated with each of the content items 204 using a deep learning generative model (e.g., Dreambooth or similar) used to fine-tune text-to-image models. The caption extractor 206 may be used to create a caption 208 for each content item 204 if one or more if the content items 204 do not have an associated caption 205 or to supplement the caption 205."; [0103] "At 902, a machine learning algorithm (e.g., software code) may be created by one or more software designers. For example, the generative AI 112 of FIGS. 1 and 3 may be created by software designers. At 904, the machine learning algorithm may be trained (e.g., fine-tuned) using pre-classified training data 906. For example, the training data 906 may have been pre-classified by humans, by machine learning, or a combination of both."
- Pub. No.: US2025/0078453 [1303] "Subsequent to training (e.g., training above an accuracy threshold) and/or fine-tuning the text generation machine learning model 4514, the model manager 4512 can use the text generation machine learning model 4514 in combination with the image generation machine learning model 4516 to generate high scoring images (e.g., based on high scoring prompts generated by the text generation machine learning model 4514)."
However, the above noted references and the ones noted in the conclusion section below, when considered singularly and in-combination, fail to reasonably teach the claims as amended on 01/23/2026. Therefore the Examiner withdraws the prior art based rejection.
Response to Applicant’s Remarks
4. Regarding 101, the Applicant notes on pages 13-14 of the response filed 01/23/2026 solution and advantages in view of a few specification paragraphs. The Examiner notes that the Applicant has not argued prong one and argues against prong two in view of the solutions and/or advantages. The Examiner respectfully disagrees because there is a clear distinction between using computer and machine learning as tools to implement an abstract idea, and setting forth a technical improvement that improves functioning of a computer and/or a technical field, the claims are directed to the former as explained in the updated rejection as set forth above in view of filed claim amendments.
Firstly, improvement to way treatments are tested such as new treatment by comparing them with the performance of similar old treatments is indeed an abstract idea. The Applicant uses machine learning to assess similarity between new and old treatments and selects the ones that exceed a similarity threshold. The selected known treatments are provided to a plurality of users to obtain/test their performance. Next, the new treatments are provided to a subset of users selected based on performance of similar old treatments For instance note SAP v. Investpic: Page 2, line 22 through Page 3, line 13 “Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting.”
And, lastly, as explained machine learning is being applied to an otherwise abstract idea when the claim is properly construed as a whole, for instance see Recentive Analytics v. Fox Corp see page 12, lines 1-4: “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.”; and as noted per the updated prong two analysis in view of the filed claim amendments, see “claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least are (as per claim 1) a trained machine-learned embedding model, finetuning the trained machine-learned embedding model, applying the trained machine-learned embedding model (to known and new treatments to compare similarity exceeding a threshold), and transmitting to client devices (of a target subset users that are targeted with new treatment, wherein the targeted subset of users are identified based on performance of known treatments) for display; (as per claim 10 in addition to claim 1 additional elements note) a non-transitory computer-readable medium, having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps; (as per claim 19 in addition to claim 1 additional elements note) a computing system, comprising: one or more processors; and a non-transitory computer-readable medium, having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps. Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements comprising machine learning model are being utilized as tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1A, 1B, 2, 4, and their associated disclosure. The processor executing the "apply it" instruction is further connected to one or more device merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Receiving and/or displaying data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes the similarity between known and new treatment and compares it with a threshold to select known treatment whose performance is evaluated by testing it on a plurality of users. Further, based on the performance of the known treatment on the plurality of users, a target subset of users is identified to be targeted with the new treatment. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is targeting content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide marketing content, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of comparing vector embeddings which constitute a vector space which are a mathematical representation of description/text/word of treatments (e.g. incentive, a coupon, or a marketing campaign) to ascertain similarity between known and new treatments exceeds a predetermined value or threshold to target new content (which incorporates new treatment e.g. text/word/description) to audience that responded positively to known treatments in the past (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
The Examiner respectfully disagrees because the analysis is evaluation of one or more additional elements singularly and in-combination, however the Applicant argues in view of abstract recitation “comparing a new treatment embedding to identify similar known treatments, accessing performance data of the similar treatments, targeting users based on that performance data” and the machine learning is being utilized as a tool, i.e. executing “apply it” instructions to carry out the abstract idea. Therefore the Examiner respectfully maintains the rejection.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
* Previously noted
- US20210192460 see [0074] "the aggregated embedding generation service 220 may also generate time-dependent aggregated embeddings that are based upon the amount of time that has passed since an entity interacted with specific job opportunity embeddings. If entity Jane Doe interacted with a first set of job opportunity content items one month ago and a second set of job opportunity content items a couple of days ago, then the aggregated embedding generation service 220 may generate separate aggregated embeddings based upon the amount of time that has passed between interactions. For example, the first set of job opportunity content items may be represented by embeddings (v.sup.1, v.sup.2, . . . , v.sup.n).sup.T1, where T1 indicates a timestamp for the interactions that are one month old and the second set of job opportunity content items may be represented by embeddings (w.sup.1, w.sup.2, . . . , w.sup.n).sup.T2, where T2 indicates a timestamp for the interactions that are two days old. The aggregated embeddings generated, based on mean pooling, may be u.sub.mean.sup.T1 and u.sub.mean.sup.T2. In an embodiment, different aggregated embeddings based on the amount of time that has passed since the interactions may be used to alter the size of the set of job opportunity content items presented to a user. For example, the size of the set of job opportunity content items similar to an older aggregated embedding may be smaller than the size of the set of job opportunity content items similar to a newer aggregated embedding
This may be beneficial to the entity since more recent entity session activity is likely to be more relevant to the entity than activity that is older. In another embodiment, aggregated embeddings based on entity session activity and the amount of time that has passed since interactions may be associated with different weight factors. For example, older aggregated embeddings may have smaller weight factors associated, while newer aggregated embeddings may have larger weight factors associated. The weight factors may be applied to scores associated with embeddings for identified job opportunity content items for recommendation, such that scores for embeddings for job opportunity content items associated with newer aggregated embeddings are increased by the associated weight factors, while scores for embeddings for job opportunity content items associated with older aggregated embeddings are decreased based on the associated weight factors."
- US2022/0366295 see [0023] For example, a data set indicating content items that were historically indicated to be relevant to users with particular features (e.g., based on historical interactions by the users with the content items) may be used along with the embeddings to generate a training data set. The training data set may comprise features of users (e.g., attributes known about users, clickstream data, and the like) associated with labels indicating embeddings of content items determined to be relevant to the users and, in some embodiments, identifiers of the relevant content items as well. The embeddings may be used to group content items with similar embeddings such that if a given user historically interacted with a first content item, and the first content item is determined to be similar to a second content item based on the embeddings (e.g., a similarity measure between the embedding of the first content item and the embedding of the second content item exceeds a threshold), then both the embedding of the first content item and the embedding of the second content item may be associated with features of the given user in the training data set.
[0028] In some embodiments, embeddings output by the model are used to identify additional content items (e.g., that may not have been included in the training data for the model) that are likely to be relevant to the user, such as based on similarity measures between embeddings. For instance, new content items may be created or become available after the training of the content prediction model. Embeddings of the new content items may be determined using the embedding model (e.g., as new content items become available), and the embeddings of the new content items may be stored and used at content prediction time by comparing them to embeddings output by the content prediction model for a given user in order to determine whether the new content items may also be relevant to the given user. Furthermore, the content prediction model may be re-trained as additional training data becomes available, such as based on new content items and/or feedback indicating content items that were in fact relevant to users (e.g., as users interact with content items recommended according to techniques described herein).
- US2024/0202798 see Abstract "Recommendation services typically struggle with sparse data scenarios. A freezing generated item start technique can use a matrix of external generated items to find a linking generated item. Embeddings can be used to determine distance between items. The technologies are useful for providing recommendations even in scenarios involving little or no transaction data."
- US20190311301 see "[0073] Machine learning modeler 120 can also segment the input data set (e.g., previous item listings) into a training set and a testing set. The training set can include a subset of the previous item listings applied to a machine learning algorithm to identify the parameters and functions for processing new item listings to achieve a target objective (e.g., generating a machine learning model). The testing set can include a subset of the previous item listings applied to a machine learning model to evaluate the efficacy of the model. In some embodiments, machine learning modeler 120 can also segment the input data set into a validation set, which can include a subset of the item listings applied to the model before testing to tune parameters of the model to avoid overfitting."
- US2020/0026772 see Abstract "feature vector associated with a candidate web document is determined. A feature space associated with the feature vector is filtered. A density value associated with the candidate web document is determined using the filtered feature space. The candidate web document is ranked with respect to a plurality of other candidate web documents based on the determined density value. The candidate web document is provided in a content feed based on the ranking"
-US2023/0021233 see [0093] "The multi-view matrix factorization model may allow predicting recommendations for new users and items, along with supporting traditional warm-start predictions for existing users and items with known history information. The cold-start recommendation tasks associated to generating recommendations for new users and/or new items may not be possible with standard collaborative filter models. The cold-start recommendation tasks may require the side-information sources to be used to predict the recommendations. The systematic integration of the side-information sources, such as the user personal data X as well as the item metadata Y illustrated in FIG. 2, may make it possible to learn the joint model as presented above. The joint model may be used to generate recommendations for completely new users (cold-start users), unseen or unused items (cold-start items) and new users-new items (cold-start users and items) collectively. For instance, the cold-start recommendation may be used to attract new users, such as new customers. Data driven personalized recommendations are better than random or top list recommendations, which may be a useful tool, for example, for advertising. The cold-start items may be utilized to target new items, such as videos or applications, to more relevant audience/users forming a candidate set for the new items."
-US2024/0020345 see [0033] The content ranking module 245 predicts the likelihood that a user will perform a conversion or other action with respect to a particular content item. In one embodiment, the content ranking module 245 provides content embeddings and user embeddings as inputs to a function that predicts a likelihood of an event given actions a user has taken in the past and given a content item or product that is a candidate for display. The content ranking module 245 may generate user embeddings based on content embeddings that represent content with which the user has previously interacted. In one embodiment, the content ranking module generates multiple user embeddings for a single user, such that each of the user embeddings represents a user's particular kind of interaction within a different categorization of content. For example, one user embedding may represent a user's searches for products within the apparel category, such as shirts and sweaters. In one example embodiment, a user embedding with respect to a particular interaction is based on a combination (e.g., average, weighted average, etc.) of the embeddings of content items the user has had that interaction with. The content embedding generator 240 and the content ranking module 245 may update the content embeddings and the user embeddings by regenerating the embeddings, for example, periodically, or when the online system 140 obtains new user or content data.
[0034] To determine what content item or product description should be displayed for a user, the content ranking module 245 applies a predictive function to the user embeddings of the user for a particular category. The predictive function may accept the user embeddings and a content embedding as input. The predictive function can generate a score that determines the likelihood of a conversion if the content is displayed to the user. In one embodiment, the likelihood scores of all candidate content items are ranked and the content item with the highest score is displayed to the user. Additional information about the content ranking module is described with respect to FIG. 5.
- US2024/0403551 see [0025] Instead of or in addition to leading a user through an upload process, the content management system 110 can access a first content item that is preexisting in the content repository for generating a new or modified description. In one example, the content management system detects that use data associated with a first content item in the content repository satisfies a criterion for generating a new description for the content item. Use data can include, for example, data indicating that a content item has been viewed, downloaded, or shared a specified number of times. A content item can instead be flagged for generating a new description if the content item has received greater than a threshold number of modifications since its existing description was generated, or if it receives a modification that is determined to be a substantive modification (e.g., if at least a specified volume of text in the content item was deleted or added). In another example, the content management system generates new content item descriptions for content items in the content repository on a periodic basis, such as once per year.
[0031] When a content item is divided into multiple portions, each of the portions can be sent to the LLM to generate a summary. A refinement algorithm is then applied to summarize the summaries from each of the portions and to generate a description for the content item as a whole based on the portion summaries. For example, the content management system 110 can generate another prompt to the LLM that causes the LLM to summarizes the portion summaries. The content management system 110 can additionally or alternatively evaluate the portion summaries to, for example, remove redundant content, identify portion summaries that have similar content (e.g., if two summaries have a cosine similarity that is greater than a first threshold), or identify portion summaries that have highly dissimilar content (e.g., if two summaries have a cosine similarity that is less than a second threshold). Rules in the content management system may then cause the system to, for example, discard a summary if it is too dissimilar to summaries (e.g., less than a threshold cosine similarity) or discard a summary that is too similar to other summaries (e.g., greater than a threshold cosine similarity), before sending any remaining summaries to the LLM for analysis.
[0044] The system 110 can automatically add the first content item to a list, or can recommend to a user that the first content item be added to a list, based on a degree of similarity between the first content item and the other content items in the list. For example, in the content upload user interface shown in FIGS. 3A-3B, a “Suggest Lists” option 340 is provided. When the option 340 is selected, the content management system 110 generates a recommendation for one or more lists to which the content item should be added. In some implementations, the system 110 uses the description of the first content item and descriptions of content items in a first list to generate vectors that represent each content item (such as embeddings generated by the LLM or by techniques such as word2vec). If a similarity metric (e.g., cosine similarity) between the vectors of the items already in the first list and the vector of the first content item is within a specified threshold, the system 110 can recommend that the first content item be added to the first list. Any of a variety of other similarity metrics can be used to determine overall similarity between two content items, including semantic or string-based methods. Some of these similarity metrics can use additional attributes or metadata associated with the content items, in addition to the descriptions of the items, in order to determine when to recommend that a content item be added to a list. Similarly, the content management system 110 can recommend that a new list be formed with the first content item and a second content item, based on a degree of similarity between the descriptions of the first and second items
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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