DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea) without significantly more.
The claims recite method, system and computer program product for providing personalized assistance to a customer.
Exemplary claim 1 recites in part,
“…obtaining user interaction data;
…receiving said user interaction data from said user computational device; and
…analyzing said user interaction data and generating personalized retail recommendations.”
The above limitations describe the steps of, 1) acquiring data, 2) analyzing the acquired data using a model, and 3) generating a result (recommendation).
The above steps describe the process of providing personalized recommendation based on analyzing user data. The above limitations, under their broadest reasonable interpretation, encompass "Mental Processes" (A Claim That Requires a Computer May Still Recite a Mental Process) enumerated in MPEP 2106.04(a)(2)(III)(C). If a claim limitation, under its broadest reasonable interpretation, covers “A Claim That Requires a Computer May Still Recite a Mental Process”, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. The claim recites additional elements in the form of computing elements (user computational device, server, AI model) to perform the limitations encompassing the identified abstract idea. The computing elements represent using a computer as a tool to perform the judicial exception as in MPEP 2106.05(f). In addition, the limitation, “training using historical user interaction data and purchase history data”, describes obtaining and processing data, which amounts to insignificant extra-solution activity that do not impose meaningful limits on the abstract idea. See MPEP 2106.05(g). Further, the limitation, “generating personalized retail recommendations for an in store experience for a physical store, when said user computational device is located within said physical store”, simply limits the abstract idea to a particular field of use. See MPEP 2106.05(h).
When considered both individually and as a whole, the additional elements do not integrate the abstract idea into a practical application.
The recitation of additional elements is acknowledged as identified above. The discussion with respect to practical application is equally applicable to consideration of whether the additional elements amount to significantly more. The computing elements represent using a computer as a tool to perform the judicial exception as in MPEP 2106.05(f). In addition, the limitation, “training using historical user interaction data and purchase history data”, while amounting to insignificant extra-solution activity, describes well‐understood, routine, and conventional computer function (performing repetitive calculations). See MPEP 2106.05(d). Further, the limitation, “generating personalized retail recommendations for an in store experience for a physical store, when said user computational device is located within said physical store”, simply limits the abstract idea to a particular field of use. See MPEP 2106.05(h).
Therefore, there are no meaningful recitations, considered in combination, that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
Accordingly, claim 1 is directed to a judicial exception (i.e., abstract idea) without significantly more.
Dependent claims 2-20 recite limitations directed to the abstract idea, and do not integrate the abstract idea into a practical application nor amount to significantly more.
For example, claims 2, 3, 5 describe displaying data, analyzing data and generating a result. Claims 7-14 describe training the AI model based on user input (feedback).
Claim Rejections - 35 USC § 102
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.
Claims 1-8, 10, 11, 15-17, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Appl. Pub. No. 2019/0228448 (Bleicher et al. – hereinafter Bleicher).
Referring to claim 1, Bleicher discloses a system for personalized retail experience based on artificial intelligence (AI), comprising
a user computational device for obtaining user interaction data; [See paragraphs 0065, 0073, 0076, 0079, 0082, 0084, 0089, 0094, 0101, 0110, 0113, 0152, 0179, 0180]
a server for receiving said user interaction data from said user computational device; and [See paragraphs 0065, 0073, 0079, 0084, 0089, 0094, 0101, 0110, 0113, 0152, 0179, 0180]
an AI model in the server for analyzing said user interaction data and generating personalized retail recommendations; [See paragraphs 0073, 0074, 0076, 0138, 0083, 0089, 0106]
wherein said AI model is trained using historical user interaction data and purchase history data; and [See paragraphs 0065, 0077, 0082, 0084, 0087, 0089, 0092, 0099, 0104, 0106]
wherein said personalized retail recommendations are generated for an in store experience for a physical store, when said user computational device is located within said physical store. [See paragraphs 0067, 0068, 0076, 0079, 0082, 0086, 0092, 0138]
Referring to claim 2, Bleicher The system of claim 1, wherein said user computational device comprises smart glasses, a smartphone, or another mobile device, or a combination thereof, for viewing information and accessing a personalized retail application for viewing said personalized retail recommendations. [See paragraphs 0076, 0079, 0113]
Referring to claim 3, Bleicher discloses the system of claim 2, wherein said user computational device comprises a user interface for displaying said personalized retail recommendations, and wherein said user interface is a mobile application that communicates with the server to receive said personalized retail recommendations. [See paragraphs 0074, 0076, 0083, 0131-0135]
Referring to claim 4, Bleicher discloses the system of claim 3, wherein said user interaction data includes one or more of user browsing history, user search queries, user product views, user product ratings, physical store product interactions, physical store product views, physical store product returns and user purchase history. [See paragraphs 0065, 0073, 0076, 0079, 0082, 0084, 0089, 0094, 0101, 0110, 0113, 0152, 0179, 0180]
Referring to claim 5, Bleicher discloses the system of claim 4, wherein said AI model uses machine learning algorithms to analyze said user interaction data and generate said personalized retail recommendations. [See paragraphs 0074, 0084, 0105]
Referring to claim 6, Bleicher discloses the system of claim 5, wherein said personalized retail recommendations include product recommendations, personalized discounts, and personalized product bundles.
Referring to claim 7, Bleicher discloses the system of claim 4, wherein said AI model is further trained using demographic data of the user. [See paragraphs 0079, 0084, 0089, 0094]
Referring to claim 8, Bleicher discloses the system of claim 7, wherein said demographic data includes one or more of user age, user gender, user location, and user preferences. [See paragraphs 0070, 0072, 0077]
Referring to claim 10, Bleicher discloses the system of claim 4, wherein said user computational device further comprises a feedback mechanism for the user to rate the relevance of said personalized retail recommendations. [See paragraphs 0073, 0079, 0080, 0084, 0089]
Referring to claim 11, Bleicher discloses the system of claim 10, wherein said feedback is used to further train and refine said AI model. [See paragraph 0073]
Referring to claim 15, Bleicher discloses the system of claim 4, wherein said server is implemented as a backend infrastructure, wherein said backend infrastructure comprises a cloud-based service, which supports access of user computational device to a plurality of services through a computer network; wherein said backend infrastructure further comprises a plurality of microservices, including a brand, product and batching management module, a user management module, and a payment profiling and notifications module. [See paragraphs 0068, 0070, 0082, 0092, 0131, 0137, 0142, 0158, 0178]
Referring to claim 16, Bleicher discloses the system of claim 15, wherein said microservices further comprise a smart glasses integration module, which supports interaction with smart glasses for said user computational device. [See paragraphs 0113, 0154, 0176]
Referring to claim 17, Bleicher discloses the system of claim 16, wherein said smart glasses integration module supports interaction with a smart glasses hardware platform. [See paragraphs 0113, 0154, 0176]
Referring to claim 19, Bleicher discloses the system of claim 15, further comprising an ERP system integration, wherein said ERP system integration supports retail store staff interactions. [See paragraphs 0079, 0084, 0094, 0101, 0109]
Referring to claim 20, Bleicher discloses the system of claim 15, wherein said server further comprises at least one microservice for supporting personalized user interactions. [See paragraphs 0065, 0073, 0076, 0079, 0082, 0084, 0089, 0094, 0101, 0110, 0113, 0152, 0154, 0176, 0179, 0180]
Claim Rejections - 35 USC § 103
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 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 9, 12-14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bleicher as applied to claim 1 above, and further in view of U.S. Patent Appl. Pub. No. 2022/0374967 (Bronicki).
Referring to claim 9, Bleicher discloses the system of claim 4 above. Bleicher does not explicitly disclose the limitation: wherein said AI model is further trained using external data sources, including market trends, seasonal trends, and product trends.
Bronicki teaches a system with the limitation: wherein said AI model is further trained using external data sources, including market trends, seasonal trends, and product trends. [See paragraphs 0218, 0219, 0421]
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified the system executing the method of Bleicher to have incorporated a shopping assistance feature as in Bronicki with the motivation of assisting customers and providing recommendation during a shopping experience. [See Bronicki paragraphs 0101, 0290, 0327; Bleicher paragraphs 0066, 0074]
Referring to claim 12, the combination of Bleicher and Bronicki discloses the system of claim 4, wherein said AI model comprises a deep learning model. [See Bronicki paragraphs 0107, 0110]
Referring to claim 13, the combination of Bleicher and Bronicki discloses the system of claim 12, wherein said deep learning model comprises a neural network. [See paragraphs 0107, 0110, 0113, 0128]
Referring to claim 14, the combination of Bleicher and Bronicki discloses the system of claim 13, wherein said neural network is a convolutional neural network. [See paragraphs 0107, 0110, 0111, 0113, 0128]
Referring to claim 18, the combination of Bleicher and Bronicki discloses the system of claim 15, wherein said personalized retail recommendations include product recommendations, personalized discounts, and personalized product bundles. [See paragraphs 0140, 0219, 0298, 0299, 0311, 0312, 0327, 0328]
Conclusion
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/OLUSEGUN GOYEA/Primary Examiner, Art Unit 3627
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