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 .
1. Applicant's preliminary amendment filed 07/29/2025 is entered. Claim 1 is canceled. New claims 2-21 are added. Claims 2-21 are pending for examination.
2. Continuity: This application filed 04/14/2025 is a continuation of 18380792, filed 10/17/2023, now U.S. Patent # 12299726, 18380792 is a continuation of 17234533, filed 04/19/2021, now U.S. Patent # 11823246, and 19178560 is a continuation of 16232907, filed 12/26/2018, now U.S. Patent # 10984461.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
3.1. Claims 2-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12299726, hereinafter patent'726. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the Patent' 726 and in the instant application recite the same subject matter directed to a system identifying a search request/user interaction from the user device interface, extracting, using a natural language algorithm, first semantic attributes in a structured format associated with a set of products, compressing data for user events from a user profile, converting the compressed data to the second semantic attributes associated with product interests of the use, converting the compressed data to the second semantic attributes associated with product interests of the user, determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user, and providing the one or more product recommendations /or generating a content presentations for one or more products to the user device based on the relevance probability, with obvious variations which do not render the claims patentably distinct from each other, for example, see below the comparison of the independent claim 1 of the Patent' 726 and the independent claim 2 of the current application, wherein the highlighted limitations of the claim 1 of the Patent'726 read on the underlined limitations of the claim 2 of the current application. The language " identifying a user interaction with a user device, the user interaction associated with content presentation on a network in at least a portion of a user interface" in claim 1 of the Patent'726 is not the same as the claim 2 language of the current application, " receiving a search by a user via a user interface of an application on a user device, wherein the search is associated with one or more product recommendations provided over a network to the user device in at least a portion of the user interface", because a search request on a user interface would require user interaction on the user interface to be identified and as such does not amount to patentably distinct reason.
Claims 1 of Patent' 726:
1. A system, comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
identifying a user interaction with a user device, the user interaction associated with content presentation on a network in at least a portion of a user interface;
extracting, using a natural language algorithm, first semantic attributes associated with a set of products from a product catalog, the natural language algorithm configured to extract the first semantic attributes in a structured format;
compressing data for user events from a user profile of a user that enables a conversion to second semantic attributes, wherein the user events are associated with time-based indications of user interest in a subset of products of the set of products;
converting the compressed data to the second semantic attributes associated with product interests of the user;
determining, based on the extracted first semantic attributes and the second semantic attributes converted from the user events from the user profile, a probability indicating a relevance of each product from the subset of products of the set of products based in part on a frequency of the user interaction on the network; and
generating a content presentation for one or more products from the subset of products based on the probability.
Claim 2 of the current Application:
2. A system, comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving a search by a user via a user interface of an application on a user device, wherein the search is associated with one or more product recommendations provided over a network to the user device in at least a portion of the user interface;
extracting, using a natural language algorithm, first semantic attributes from information associated with a plurality of products purchasable from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format;
determining compressed data from a recommendation engine, wherein the compressed data comprises structured data for user events or a user profile associated with the user compressed by the recommendation engine;
converting the compressed data to second semantic attributes associated with interests of the user based on the user events or the user profile;
determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user; and
providing the one or more product recommendations to the user device in the at least the portion of the user interface based on the relevance probability of each product to the interests of the user.
The limitations of the dependent claims 4, 5, 6, 7, 8,9, and 10 from claim 2 of the current application recite similar limitations which are not patentably distinct from the limitations recited in claims 2-3, 4, 5, 1 and 7, 7, 13, and 1 respectively of the Patent'726. Regarding dependent claim 3 from claim 2 of the current application, it recites determining information of the products from an online inventory of the merchant, which is not patentably distinct from determining information of products from the content presentation on a network, as recited in claim 1 of the Patent' 726.
Since the limitations of the claims 11-19 of the current application are similar to the limitations of claims 2-10 of the current application, they are analyzed on the same basis as rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12299726, hereinafter patent'726.
Regarding independent claim 20 of the current application, the underlined limitations, " A non-transitory machine-readable medium having stored thereon machine- readable instructions executable to cause a machine to perform operations comprising: extracting, using a natural language algorithm, first semantic attributes from information associated with a plurality of items available from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format; determining second semantic attributes associated with an interest of a user based on compressed data from a recommendation engine from a user profile associated with a user; computing probabilities indicating relevancies of the plurality of items to the interest of the user; determining a first subset of the plurality of items that are more relevant to the interest of the user than a second subset of the plurality of items based on the probabilities; and providing at least one recommendation of the first subset of the plurality of items to the user over a network in at least a portion of a user interface of a user device associated with the user. ", are already covered by the highlighted limitations in claim 1 of the Patent'726. The limitations, " determining a first subset of the plurality of items that are more relevant to the interest of the user than a second subset of the plurality of items based on the probabilities"; are obvious in view of the limitations of claims 7 and 13 of the Patent'726: "7. The system of claim 1, wherein the content presentation includes two or more products from the subset of products, and wherein generating the content presentation further comprises: ranking the two or more products within the content presentation based on the probability indicating the relevance of each product. 8. The method of claim 8, wherein generating the content presentation further comprises: ranking the two or more products within the content presentation based on the probability indicating the relevance of each product, and wherein the at least one product of the two or more products is selected based on the probability and the ranking of the two or more products.".
Regarding the dependent claim 21 from claim 20, its limitations, are obvious in view of the claim 1 of the Patent' 726.
3.2 Claims 2-3, 6-10, 11-12, 15-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, and 16 of U.S. Patent No. 11823246, hereinafter Patent ‘246 in view of Chen et al. [US Patent 11080918 B2], hereinafter Chen and cited in the IDS filed 04/14/2025.
Claim 2 of the current Application:
2. A system, comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving a search by a user via a user interface of an application on a user device, wherein the search is associated with one or more product recommendations provided over a network to the user device in at least a portion of the user interface;
extracting, using a natural language algorithm, first semantic attributes from information associated with a plurality of products purchasable from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format;
determining compressed data from a recommendation engine, wherein the compressed data comprises structured data for user events or a user profile associated with the user compressed by the recommendation engine;
converting the compressed data to second semantic attributes associated with interests of the user based on the user events or the user profile;
determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user; and
providing the one or more product recommendations to the user device in the at least the portion of the user interface based on the relevance probability of each product to the interests of the user.
Claim 1 of Patent’ 246 recites:
1. A system comprising:
one or more processors; and one or more machine-readable storage media having instructions stored thereon that, in response to being executed by the one or more processors, cause the system to perform operations comprising:
receiving, from a user device, a request for a content-based recommendation;
retrieving product attributes based on data received from the request;
determining a probability that a product is relevant to a user profile for each product in a plurality of products, wherein the determining comprises:
extracting, in a format from unstructured data in a product catalog for the plurality of products, structured data for semantic attributes for the retrieved product attributes in the product catalog, compressing the structured data for the semantic attributes, converting data for the user profile to the semantic attributes, determining a plurality of events performed by the user over a time period, weighting the products in the converted data based on a term frequency for each of the plurality of products occurring in the user profile during the time period based on the plurality of events, and
generating the probability that the product is relevant to the user profile based on an estimation calculated using mappings from the semantic attributes in the compressed structured data for the product catalog and the converted data including the weighted products in the user profile; and
presenting the content-based recommendation having a set of products from the plurality of products based in part on a ranking of the probability associated with each product in the plurality of products.
Claim 1 of the Patent’246 [see the highlighted limitations above] teaches the underlined limitations claim 2 of the instant application, but does not explicitly teach using natural language processing to extract the semantic attributes in a structured format from the product catalog. Chen, in the same field of predicting and comparing product such as garments attributes, teaches [see Chen claim 9, " keyword extraction or natural language processing (NLP) is used to extract style-related attributes and semantic labels from training garment name and training garment description text, wherein the style-related attributes are multiple-class discrete attributes, binary discrete attributes, or continuous attributes."] uses natural language processing for extracting semantic attributes so that proper matching and comparison can be made. Therefore, in view of the teachings of Chen in the same field of predicting and comparing product such as garments attributes, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patent'246 to incorporate the concept of using natural language processing to extract the semantic attributes in a structured format from information associated with a plurality of products purchasable from a merchant, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Limitations of dependent claims 3, 6-10 of the current application, are obvious and not patentably distinct from the limitations of claim 1,4, 6, and 13, 16 of the Patent’246.
Since the limitations of claims 11-12, 15-19 of the current application are similar to the limitations covered for claims 2-3, 6-10, they are analyzed and rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 6, 13 and 16 of Patent ‘246 in view of Chen based on same rationale established for claims 2-3, 6-10. Since the limitations of claims 20-21 of the current application are similar and not patentably distinct form the limitations of claims 2, and 7 of the current application, they are and rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, and 7 of Patent ‘246 in view of Chen based on same rationale established for claims 2, and 7.
4. Claims 2-10
Subject Matter Eligibility:
Claim 2 recites:
2. A system, comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
(i) receiving a search by a user via a user interface of an application on a user device, wherein the search is associated with one or more product recommendations provided over a network to the user device in at least a portion of the user interface;
(ii) extracting, using a natural language algorithm, first semantic attributes from information associated with a plurality of products purchasable from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format;
(iii) determining compressed data from a recommendation engine, wherein the compressed data comprises structured data for user events or a user profile associated with the user compressed by the recommendation engine;
(iv) converting the compressed data to second semantic attributes associated with interests of the user based on the user events or the user profile;
(v) determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user; and
(vi) providing the one or more product recommendations to the user device in the at least the portion of the user interface based on the relevance probability of each product to the interests of the user.
Step 1 analysis:
Claims 2-10 are to a system /apparatus, which are statutory (Step 1: Yes).
Step 2A Analysis:
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The highlighted limitations of claim 2 comprising, " extracting first semantic attributes from information associated with a plurality of products purchasable from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format; and determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user", under their broadest reasonable interpretation, fall within mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. That is, other than reciting “by a processor” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “by the processor” language, the claim encompasses a person looking at product information of a merchant and recognize/identify and organize [structural format] the semantic attributes/characteristics and based on the semantic attributes determine a relevance probability matching the interests of a user. The mere nominal recitation of by a processor does not take the claim limitations out of the mental process grouping. Thus, the claim 2 and its dependent claims 3-10 recite a mental process.
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 2-10:
The claim 2 recites a combination of additional elements including" one or more processors executing the steps of receiving a search request via a user interface of an application on a user device, wherein the search is associated with one or more product recommendations provided over a network to the user device in at least a portion of the user interface, extracting, using a natural language algorithm, first semantic attributes from information associated with a plurality of products purchasable from a merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format, determining compressed data from a recommendation engine, wherein the compressed data comprises structured data for user events or a user profile associated with the user compressed by the recommendation engine, converting the compressed data to second semantic attributes associated with interests of the user based on the user events or the user profile, determining, based on the first semantic attributes and the second semantic attributes, a relevance probability of each of the plurality of products to the interests of the user, and providing the one or more product recommendations to the user device in the at least the portion of the user interface based on the relevance probability of each product to the interests of the user". The claim as a whole integrates the method of "Mental Process" into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by providing network users to present a user with customizable content tailored to a user’s interest and likes and avoid spending time and computer resources looking through products and/or services on websites that may not be of interest. As such, this may result in avoiding a loss of time and purchase for a user and consequently to a loss of a sale to the merchant [See Specification paragraph 0003].
Step 2B: Not Applicable.
Claims 2-10 are not directed to an abstract idea and are patent eligible.
Claim Rejections - 35 USC § 101
5. 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 11-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 11-19 are to a process comprising a series of steps, and clams 20-21 to manufacture, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 11 recites:
A method, comprising:
receiving an indication to provide a recommendation to a user that is associated with one or more of a plurality of items available from a merchant;
extracting, using a natural language algorithm, first semantic attributes from information associated with the plurality of items available from the merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format;
determining second semantic attributes associated with interests of the user based on data from a recommendation engine, wherein the data comprises compressed data previously transformed by the recommendation engine from user events or a user profile associated with the user;
determining, based on the first semantic attributes and the second semantic attributes, probabilities of relevance of the plurality of items to the interests of the user;
generating the recommendation to the user of a first item of the plurality of items based on the probabilities of relevance; and
providing the recommendation to the user over a network in at least a portion of a
user interface of a user device associated with the user.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 11-21 recite abstract idea.
The highlighted limitations of claim 2 comprising, " extracting, using a natural language algorithm, first semantic attributes from information associated with the plurality of items available from the merchant, wherein the natural language algorithm is configured to extract the first semantic attributes in a structured format; determining second semantic attributes associated with interests of the user, wherein the data comprises compressed data from user events or a user profile associated with the user; determining, based on the first semantic attributes and the second semantic attributes, probabilities of relevance of the plurality of items to the interests of the user; generating the recommendation to the user of a first item of the plurality of items based on the probabilities of relevance;", under their broadest reasonable interpretation, fall within mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Since the claim, as drafted, recites no devices or computer or processor or machine for executing these steps, nothing in the claim elements precludes the steps from practically being performed in the mind and manually using a pen and paper. For example, but for the “by the processor” language, the claim encompasses a person looking at items information of a merchant and recognize/identify and organize [structural format] the semantic attributes/characteristics and also determine semantic interests from a user's profile or interests then based on these semantic attributes determine a relevance probability matching the interests of a user so that recommendation for one or more items is generated based on the probabilities of relevance. Thus claim 11, and its dependent claims 12-19 recite "Mental Processes".
Since the other independent claim 20 recites similar limitations as claim11, it with its dependent claim 21 are analyzed on the same basis reciting "Mental Processes"
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 11-21: The judicial exception is not integrated into a practical application.
Claim 11 recites the additional elements of: "receiving an indication to provide a recommendation to a user that is associated with one or more of a plurality of items available from a merchant"; "determining second semantic attributes associated with interests of the user based on data from a recommendation engine, wherein the data comprises compressed data previously transformed by the recommendation engine from user events or a user profile associated with the user;", and providing the recommendation to the user over a network in at least a portion of a user interface of a user device associated with the user."
The "receiving …" and "providing…." steps are mere data gathering and transmitting steps, without reciting as how these are implemented, and thus amount to non-significant extra-solution activity and do not add any meaningful limits on practicing the abstract idea.
The limitations, " determining second semantic attributes associated with interests of the user based on data from a recommendation engine, wherein the data comprises compressed data previously transformed by the recommendation engine from user events or a user profile associated with the user;", recite the use of a recommendation engine , which, under its broadest reasonable interpretation, implies a generic software used to compress data and as such it does not integrate the abstract into a practical application because they do not add any meaningful limits on practicing the abstract idea.
Accordingly, even in combination, the additional elements in claim 11 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim 11 is directed to an abstract idea.
Since the limitations of the other independent claims recite similar steps of extracting first semantic attributes, determining second semantic attributes, computing probabilities, determining first subset of the plurality of items that are more relevant, as claim 1, fall within mental processes. The difference is that the claim 20 recites using a processor to implement these steps, but the processor is recited at a high level of generality and is merely used as a tool to implement abstract idea. See MPEP 2106.05(f). The step of providing recommendation is a mere data transmitting which is non-significant extra-solution activity and the computer is used as a tool to implement a generic computer function of transmitting data. See MPEP 2106.05(f). Accordingly, even in combination, the additional elements in claim 20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim 20 is directed to an abstract idea.
Examiner has reviewed the dependent claims 12-19, which merely expand the scope of the limitations considered for claim 1 reciting non-functional descriptive data, limitations that fall within mental processes, and generic data compressing function, which do not impose any meaningful limits on practicing the abstract idea. Therefore, dependent claims 12-19, similar to claim 1 are directed to an abstract idea.
Dependent claim 21 recites computing the probabilities based on one or more interactions over time, which is a manual activity and does not integrate the abstract idea into a practical application, because it does not impose any meaningful limits on practicing the abstract idea. Claim 21 is directed to an abstract idea.
Even when viewed in combination, the additional elements in claims 11-21 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claims 11-21 are directed to the judicial exception. (Step 2A: YES). Step 2A=Yes.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 11-21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claim 9s) recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 11-21 amount to no more than mere instructions to apply the exception using a generic computer component, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer component, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Additional elements including receiving and providing data were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/outputting/displaying/ presenting data. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/displaying/ presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2.
See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered in combination, the additional elements in claims 11-21 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
6. Prior art discussion:
Claims 2-10
Regarding claim 2, the prior art of record, neither teaches nor renders obvious, as a whole, at least the limitations comprising "one or more processors executing extracting, using a natural language algorithm, first semantic attributes in a structured format from information associated with a plurality of products purchasable from a merchant, determining compressed data comprising structured data for user events or a user profile associated with the user from a recommendation engine, and converting the compressed data to second semantic attributes associated with interests of the user based on the user events or the user profile;" in combination with the rest of the limitations of the claim 2. Claims 3-10 depend from claim 2.
Claims 11-19:
Regarding claim 11, the prior art of record, neither teaches nor renders obvious, as a whole, at least the limitations comprising " extracting, using a natural language algorithm, first semantic attributes in a structured format from information associated with a plurality of items available from a merchant, determining second semantic attributes associated with interests of the user based on compressed data previously transformed by a recommendation engine from user events or user profile associated with the user" in combination with the rest of the limitations of the claim 11. Claims 12-19 depend from claim 11.
Claims 20-21
Regarding claim 20, the prior art of record, neither teaches nor renders obvious at least the limitations, as a whole, comprising "a machine extracting, using a natural language algorithm, first semantic attributes in a structured format from information associated with a plurality of items available from a merchant, determining second semantic attributes associated with an interest of a user based on compressed data from a recommendation engine from a user's profile associated with a user" in combination with the rest of the limitations of the claim 20. Claim 21 depends from claim 20.
7. Discussion of the most relevant prior art: The following references have been identified as the most relevant prior art to the claimed invention.
(i) Lotikar et al. [US 9286362 B2; see claim 1] describes a system and a method comprising storing, in the meta-data repository, at least one meta-data item belonging to each of an element type and a relationship type meta-data in a data repository, capturing a relationship between the at least one meta-data item and context in which the relationship was generated, extracting, from data items stored in said data repository, said meta-data items, aggregating data and meta-data items and relationships thereof across one or more users in a structured file format, parsing the structured file format to extract data representing concepts, and to extract relationships, and permitting a user, using a profile tool, to select relationships from the extracted relationships, recording said user's selections as a profile for that user so that relevant data can be retrieved in response to a user query comprising at least requested data items by parsing said user profile to dynamically determine kinds of relationships a user wants to consider.
(ii) ITO et al. [US 20160162767 A1; see para 0138 and Fig.3] describes that there are known techniques to compress input data and convert it into data words, which are stored in a data storing region.
(iii) Phillips et al. [US 10579835 B1; see col.14, line 64-col.15, line 7] describes
a system generating and storing one or more semantic interpretations 142 of the reference expression(s) analyzed at blocks 314, and the natural language model is used to create the semantic interpretations, which determine the data values for the attributes of the identified semantic type based on the semantic units extracted or derived from the reference expression 218.
(iv) Kanigsberg et al. [US 20140289239 A1 cited in the IDS filed 04/25/2025; see abstract) describes a search technology generating recommendations based on semantic attributes extracted from user profiles using natural language processing and based on the user's profile information, the system determines which ad is best suited to a given profile and delivers that ad.
(v) Pinel et al. [US 20150199743 A1 cited in the IDS filed 04/25/2025; see Abstract and paras 0013, 0016] describes making content-based recommendations for products based on profiles
(vi) Chari et al. [US patent 9769208 B2, see Abstract, A1 cited in the IDS filed 04/25/2025] teaches extracting semantic attributes using natural language process from freeform text descriptions of the subjects.
Foreign references:
(vii) CN 101073254; see para 0054] describes that the parsed data is converted
to compressed data and semantic equivalent expression.
(viii) BY 349 C1 describes that there are known methods for compressing a binary code for transmission over communication channels and include converting the source binary code into a compressed data stream using orthogonal functions.
NPL reference:
(ix) R. Ghani and A. E. Fano, "Using text mining to infer semantic attributes for retail data mining," 2002 IEEE International Conference on Data Mining, 2002. Proceedings., Maebashi City, Japan, 2002, pp. 195-202; retrieved from IP. Com on 07/20/2026 describes that in many domains, semantic information is implicitly available and can be extracted automatically to improve data mining systems, wherein the system is trained to extract semantic features for apparel products and populate a knowledge base with these products and features by applying text learning techniques to the descriptions obtained from websites of retailers and this can help to build recommender systems and build a knowledge base with accurate facts which can then be used to create profiles of individual customers, groups of customers, or entire retail stores.
Conclusion
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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