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 .
DETAILED ACTION
Status of Claims
This action is a first action on the merits in response to the application filed on 03/31/2023.
Claims 1-9 have been withdrawn. Claims 10-20 are currently pending and have been examined in this application.
Examiner acknowledges Applicant’s election of Group II, claims 10-20 without traverse.
Claim Objections
Claims 11 and 12 are objected to for the following informalities. Claim 11 recites, “The media of claim 10, where in the products…” at lines 1-2. The preamble should be consistent with the preamble of claim 10 to include ‘one or more computer-readable media’. Claim 12 is objected to under the same rationale.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 10 recites:
obtaining customer data for each customer in a set of customers, wherein the customer data includes subject matter indicating environmental behavior for each customer;
classifying, via a machine learning model, each customer into a set of segments based on each customer's corresponding customer data, wherein the set of segments correlate to the likelihood of the customer engaging in positive environmental behavior; and
providing products for presentation based on at least one segment in the set of segments.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to sales behavior but for the recitation of generic computer components (e.g. a processor). For example, obtaining customer data including environmental behavior, classifying customers into segments and providing products based on the segment is related to sales activities. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data.
Claim 18 recites:
generating, via a first machine learning model, a score for each product in a set of products, wherein the score for the product is correlated to the environmental effect of the product;
classifying, via a second machine learning model, a customer into a segment of a set of segments, wherein the set of segments correlate to the likelihood of the customer engaging in positive environmental behavior;
receiving a search query from the customer;
determining a responsive set of products from the set of products in response to the search query;
ranking the responsive set of products based on the score of each product in the responsive set of products and based on the segment of the customer;
and providing the ranked responsive set of products in response to the search query.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to sales behavior but for the recitation of generic computer components (e.g. a processor). For example, obtaining customer data including environmental behavior, classifying customers into segments and providing products based on the segment is related to sales activities. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data.
The dependent claims encompass the same abstract ideas. For instance, Claim 11 is directed to presenting products to customer based on search; Claim 12 is directed to generating a score for each product and presenting the products; Claim 13 is directed to a second ML model trained by a set of product descriptions; Claim 14 is directed to receiving a search query, determining and ranking products; Claim 15 is directed to ranking products based on product score and re-ranking; Claim 16 customer data includes demographic, identifying ranked products that were purchased by another customer; Claim 17 is directed to providing a recommended product ranked using product score and threshold value; Claim 19 is directed to training set of product descriptions and classifying customers and Claim 20 is directed to ranking the products based on product score and re-ranking based on user segments.
The judicial exceptions are not integrated into a practical application. Claim 10 recites the additional elements of one or more computer-readable media and a processor. Claim 18 recites the additional elements a non-transitory computer readable medium and a processor. These are generic computer components recited at a high level of generality as performing generic computer functions (Spec see ¶0107-¶0108).
For instance, the steps of obtaining customer data for each customer in a set of customers (data gathering), classifying via machine learning model each customer into a segment (analyzing data) and providing products for presentation based on the segment illustrate collecting and analyzing data to produce a result (presenting products).
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor and a crm are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 10-20 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 10 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Peng (US 2018/0240158).
Claim 10:
Peng discloses:
One or more computer-readable media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising: (see at least Figure 4 and ¶0032, service with processor and memory)
obtaining customer data for each customer in a set of customers, wherein the customer data includes subject matter indicating environmental behavior for each customer; (see at least ¶0035, profile and classify customers based on their interests including using machine learning and user preferences to identify user’s interest in eco-friendly activities)
classifying, via a machine learning model, each customer into a set of segments based on each customer's corresponding customer data, wherein the set of segments correlate to the likelihood of the customer engaging in positive environmental behavior; and (see at least ¶0035, profile and classify customers based on their interests including using machine learning and user preferences to identify user’s interest in eco-friendly activities; see also ¶0026, performing customer profiling using machine learning for processing and categorizing information related to activities and interests of the user)
providing products for presentation based on at least one segment in the set of segments. (see at least ¶0012, recommending a plurality of products based on customer profiling and classification; see also ¶0027)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 11-14, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peng (US 2018/0240158)in view of Takawale et al. (US 2024/0070680).
Claim 11:
While Peng discloses claim 10, Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
wherein the products are provided for presentation to a customer searching for products or a business providing the products. (see at least ¶0022, user searching for topic and a subset of products recommendation are displayed)
Claim 12:
While Peng and Takawale disclose claim 10, Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
wherein the method further comprises: generating, via a second machine learning model, a score for each product in a set of products, wherein the score for the product is correlated to the environmental effect of the product; and (see at least ¶0042, determining a product sustainability score based on product’s impact on the environment; see also ¶0050)
wherein the products are provided for presentation further based on the score for the products. (see at least ¶0042-¶0044, product recommendation is presented to the user)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng with the product sustainability scoring and product recommendation of Takawale in order to apply machine learning to generate sustainability scores and curate product recommendations (Spec see ¶0001).
Claim 13:
While Peng and Takawale disclose claim 12, Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
wherein the second machine learning model is trained by a set of product descriptions, wherein each product description in the set of product descriptions includes subject matter indicating an environmental effect of a corresponding product. (see at least ¶0050, training sustainability attribute model uses specific sustainability attributes of products)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng with the product sustainability scoring and product recommendation of Takawale in order to apply machine learning to generate sustainability scores and curate product recommendations (Spec see ¶0001).
Claim 14:
While Peng and Takawale disclose claim 12, Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
wherein the method further comprises: receiving a search query from a customer; (see at least Figure 1 and associated text; see also ¶0022, user search)
determining a responsive set of products from the set of products in response to the search query; (see at least Figure 1 and associated text; see also ¶0022, user search is input by pull down menu selecting product categories, upon selection the system generates sustainability scores for a set or products and product recommendations are presented)
ranking the responsive set of products based on the score of each product in the responsive set of products and segment of the customer; and (see at least ¶0006, user display a subset of products ranked in order of sustainability scores from highest to lowest; see also ¶0022)
wherein the products are provided for presentation to the customer and comprises providing the ranked responsive set of products in response to the search query. (see at least ¶0022, user performs a search and ranked products are presented highest to lowest based on sustainability scores)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng with the product sustainability scoring and product recommendation of Takawale in order to apply machine learning to generate sustainability scores and curate product recommendations (Spec see ¶0001).
Claim 18:
Peng discloses:
A computing system comprising: a processor; and a non-transitory computer-readable medium having stored thereon instructions that when executed by the processor, cause the processor to perform operations including: (see at least Figure 4 and ¶0032, service with processor and memory)
classifying, via a second machine learning model, a customer into a segment of a set of segments, wherein the set of segments correlate to the likelihood of the customer engaging in positive environmental behavior; (see at least ¶0035, profile and classify customers based on their interests including using machine learning and user preferences to identify user’s interest in eco-friendly activities; see also ¶0026, performing customer profiling using machine learning for processing and categorizing information related to activities and interests of the user; see also )
receiving a search query from the customer; determining a responsive set of products from the set of products in response to the search query; (see at least ¶0035, a customer browsing, customer profiling occurs and is processed thru product recommendation to produce results)
ranking the responsive set of products based on the score of each product in the responsive set of products and based on the segment of the customer; and
providing the ranked responsive set of products in response to the search query.
While Peng discloses the above limitations, Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
generating, via a first machine learning model, a score for each product in a set of products, wherein the score for the product is correlated to the environmental effect of the product; (see at least ¶0042, determining a product sustainability score based on product’s impact on the environment; see also ¶0050)
receiving a search query from the customer; (see at least Figure 1 and associated text; see also ¶0022, user search)
determining a responsive set of products from the set of products in response to the search query; (see at least Figure 1 and associated text; see also ¶0022, user search is input by pull down menu selecting product categories, upon selection the system generates sustainability scores for a set or products and product recommendations are presented)
ranking the responsive set of products based on the score of each product in the responsive set of products and based on the segment of the customer; and and (see at least ¶0006, user display a subset of products ranked in order of sustainability scores from highest to lowest; see also ¶0022)
providing the ranked responsive set of products in response to the search query. (see at least ¶0022, user performs a search and ranked products are presented highest to lowest based on sustainability scores)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng with the product sustainability scoring and product recommendation of Takawale in order to apply machine learning to generate sustainability scores and curate product recommendations (Spec see ¶0001).
Claim 19:
While Peng and Takawale disclose claim 18, Peng further discloses the second machine learning model classifies each customer in a set of customers into the set of segments through customer data for each customer in the set of customers, wherein the customer data includes subject matter indicating environmental behavior for each customer (see at least ¶0035, profile and classify customers based on their interests including using machine learning and user preferences to identify user’s interest in eco-friendly activities; see also ¶0026, performing customer profiling using machine learning for processing and categorizing information related to activities and interests of the user), Peng does not explicitly disclose the following limitation; however, Takawale does disclose:
wherein: the first machine learning model is trained by a set of product descriptions, wherein each product description in the set of product descriptions includes subject matter indicating an environmental effect of a corresponding product; and (see at least ¶0050, training sustainability attribute model uses specific sustainability attributes of products)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng with the product sustainability scoring and product recommendation of Takawale in order to apply machine learning to generate sustainability scores and curate product recommendations (Spec see ¶0001).
Claims 15, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Peng (US 2018/0240158)in view of Takawale et al. (US 2024/0070680) further in view Gudla et al. (US 2024/0104622).
Claim 15:
While Peng and Takawale disclose claim 14, and Takawale discloses wherein the ranking comprises: initially ranking the responsive set of products based on the score of each product in the responsive set of products and (see at least ¶0006, user display a subset of products ranked in order of sustainability scores from highest to lowest; see also ¶0022), neither explicitly disclose the following limitations; however, Gudla does disclose:
wherein the ranking comprises: initially ranking the responsive set of products based on the score of each product in the responsive set of products; and (see at least Abstract and ¶0003, a score is computed for each item and the score is boosted based on a user segment then ranked based on boosted score; see also ¶0035, items are scored and ranked based on scores)
re-ranking the initially ranked responsive set of products based on the segment of the customer. (see at least Abstract and ¶0003, a score is computed for each item and the score is boosted based on a user segment then ranked based on boosted score)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng and the product sustainability scoring and product recommendation of Takawale with the ranking based on item scores and user segment of Gudla in order to provide ranked product rankings based on similar users.
Claim 16:
While Peng and Takawale disclose claim 14, Peng further discloses wherein the method further comprises: wherein the customer data for each customer in a set of customers further comprises demographic data; (see at least ¶0029, users register on a website using personal, location and contact information) and wherein at least a portion of the demographic data of the different customer is similar to demographic data of the customer (see at least ¶0035, profile and classify customers based on their interests including using machine learning and user preferences to identify user’s interest in eco-friendly activities); neither explicitly disclose the following limitations; however, Gudla discloses:
identifying a product in the ranked responsive set of products above a threshold ranking purchased by a different customer in the set of customers, wherein at least a portion of the demographic data of the different customer is similar to demographic data of the customer; and (see at least ¶0044, select items with boosted scores or ranks that exceed some threshold for presentation to customer; see also ¶0044, boosting item scores based on user segments and item category or brand and product ranking based on boosted scores, where the customer segment indicates different customers that may have purchased the same or similar items; see also ¶0058, user segments associated with customer purchasing behavior)
wherein the providing products for presentation to the customer further comprises providing an indication that the product was purchased by others with similar demographic data. (see at least Figure 3 and ¶0072, presenting selected items based on boosted sores or ranks that exceed a threshold)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng and the product sustainability scoring and product recommendation of Takawale with the ranking based on item scores and user segment of Gudla in order to provide ranked product rankings based on similar users.
Claim 20:
While Peng and Takawale disclose claim 18, and Takawale discloses wherein the ranking comprises: initially ranking the responsive set of products based on the score of each product in the responsive set of products and (see at least ¶0006, user display a subset of products ranked in order of sustainability scores from highest to lowest; see also ¶0022), neither explicitly disclose the following limitations; however, Gudla does disclose:
wherein the ranking comprises: initially ranking the responsive set of products based on the score of each product in the responsive set of products; and (see at least Abstract and ¶0003, a score is computed for each item and the score is boosted based on a user segment then ranked based on boosted score; see also ¶0035, items are scored and ranked based on scores)
re-ranking the initially ranked responsive set of products based on the segment of the customer. (see at least Abstract and ¶0003, a score is computed for each item and the score is boosted based on a user segment then ranked based on boosted score)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng and the product sustainability scoring and product recommendation of Takawale with the ranking based on item scores and user segment of Gudla in order to provide ranked product rankings based on similar users.
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Peng (US 2018/0240158)in view of Takawale et al. (US 2024/0070680) further in view Gudla et al. (US 2024/0104622) further in view of McAllister et al. (US 10332181)
Claim 17:
Peng and Takawale disclose claim 14, neither explicitly disclose the following limitation; however, Gulda does disclose:
wherein the providing products for presentation to the customer further comprises: providing a recommended product in response to the search query, wherein the recommended product is a product in the ranked responsive set of products whose score is above a threshold value; and (see at least ¶0035, ranking results based on scores meeting a threshold)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng and the product sustainability scoring and product recommendation of Takawale with the ranking based on item scores and user segment of Gudla in order to provide ranked product rankings based on similar users.
While Peng, Takawale and Gudla disclose the above limitations, neither explicitly disclose the following limitation; however, McAllister does disclose:
further providing a decoy product in response to the search query, wherein the decoy product is a different product in the ranked responsive set of products whose score is below the threshold value and a price of the different product is above a threshold price. (see at least column 4, lines 55-67-column 5, lines 1-9, results of search are ranked based on such as meet or exceed a price threshold or other requirement and may be ranked more highly or emphasized more; see also column 8, lines 24-25, a list of recommendations displayed to a user may include substitutable items)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the customer segmentation based eco-friendly tendencies of Peng, the product sustainability scoring and product recommendation of Takawale and the ranking based on item scores and user segment of Gudla with the substitutable items included in search results of McAllister to provide recommended items based on various factors.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Doner et al. (US 2024/0265484) discloses determining a query result set of a plurality of result products, in response to the search query, determining a subset of the plurality of result products that satisfy at least one of environmental, social, governance (ESG) factors or diversity factors and ranking the subset of the plurality of result products.
Zhu et al. (US 2023/0089850) discloses identifying environmental impact components associated with a product, calculating the environmental impact value for each of the environmental impact components to generate a plurality of environmental impact values and scoring the product to determine an impact score.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
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