DETAILED ACTION
Status of Claims
This is a first office action on the merits in response to the application filed on12/20/2024.
Claims 1-20 are currently pending and have been examined.
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 .
Priority
This application claims priority of PCT Application PCT/CN2023/101248 filed on 6/20/2023 and further claims priority of Chinese application CN202210705920.8 filed on 6/21/2022. Applicant's claim for the benefit of this prior-filed application is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/29/2025 and 10/14/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-12 are directed toward a process and claims 13-20 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a method comprising: inputting N pieces of first information into a to-be-trained model to obtain a predicted item recommendation result, wherein the to-be-trained model is configured to: obtain the N pieces of first information, wherein an i.sup.th piece of the first information indicates an i.sup.th first item and an i.sup.th behavior, wherein the i.sup.th behavior is a behavior of a user for the i.sup.th first item, wherein N behaviors of the user correspond to M categories, and wherein i=1, . . . , N, N≥M, and M>1; process the N pieces of first information based on a multi-head self-attention mechanism to obtain N pieces of second information; obtain the predicted item recommendation result based on the N pieces of second information; determine, based on the predicted item recommendation result, a target item from K second items, wherein K≥1; and recommend the target item to the user; obtaining a target loss based on the predicted item recommendation result and a real item recommendation result, wherein the target loss indicates a difference between the predicted item recommendation result and the real item recommendation result; and obtaining a target model by updating a parameter of the to-be-trained model based on the target loss until a model training condition is met (Organizing Human Activity and Mathematical Relationships), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing information to determine behaviors of the users, which is then used to predict item recommendations for the users and predict target loss between the predicted recommendation and the real recommendation and training the model to minimize loss, which is managing how users interact for commercial purposes of minimizing loss. The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Mathematical Relationships because the claimed limitations recite specific mathematical relation ships for performing predictions and recommendations on products to minimize loss.
Dependent claims 2-6, 8-12, and 14-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: Claim 1 recites no additional elements. In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “a to-be-trained model (claim 7)”, “an apparatus, comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to cause the apparatus to (claim 13)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-6, 8-12, and 14-20 further narrow the abstract idea and recite no additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)). The dependent claims merely further define the analysis of how the predictions are determined, narrowing the abstract idea.
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, Method; System Independent claims 7 and 13 recite “a to-be-trained model (claim 7)”, “an apparatus, comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to cause the apparatus to (claim 13)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0078, 0184, and 0193-0194 and Figures 2A, 2C, 3, and 10-11. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-6, 8-12, and 14-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The dependent claims recite no additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 1-2, 5-8, 11-14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (US 2020/0242450 A1) in view of Brundage et al. (US 2018/0137143 A1).
Regarding Claims 1, 7, and 13: Tang et al. teach a method comprising (See Figure 2):
inputting N pieces of first information into a to-be-trained model to obtain a predicted item recommendation result, wherein the to-be-trained model is configured to (See Figure 2, Figure 3, Figure 9, Paragraph 0069, and claim 1):
obtain the N pieces of first information, wherein an i.sup.th piece of the first information indicates an i.sup.th first item and an i.sup.th behavior, wherein the i.sup.th behavior is a behavior of a user for the i.sup.th first item, wherein N behaviors of the user correspond to M categories, and wherein i=1, . . . , N, N≥M, and M>1 (See Figure 1, Figure 2, Paragraphs 0070-0073, Paragraphs 0085-0088, Paragraph 0100, and claim 1);
process the N pieces of first information based on a multi-head self-attention mechanism to obtain N pieces of second information (See Figure 2, Figure 4, Figure 5, Paragraphs 0072-0073, Paragraph 0100, Paragraphs 0123-0125, and claim 1);
obtain the predicted item recommendation result based on the N pieces of second information; determine, based on the predicted item recommendation result, a item from K second items, wherein K≥1; and recommend the item to the user (See Figure 2, Figure 4, Figure 5, Paragraphs 0069-0073, Paragraphs 0085-0088, Paragraph 0100, Paragraphs 0123-0125, claim 1, and claim 3);
obtaining a target loss based on the predicted item recommendation result and a real item recommendation result, wherein the target loss indicates a difference between the predicted item recommendation result and the real item recommendation result (See Figure 2, Figures 4-5, Paragraphs 0131-0134, Paragraphs 0179-0180, and claim 1);
and obtaining a target model by updating a parameter of the to-be-trained model based on the target loss until a model training condition is met (See Figure 8, Paragraph 0070, Paragraphs 0072-0073, Paragraph 0123, Paragraph 0147, and Paragraph 0155).
Tang et al. do not specifically disclose a target item; recommend the target item to the user. However, Brundage et al. further teach a target item; recommend the target item to the user (See Paragraph 0010, Paragraph 0077, Paragraph 0085, Paragraph 0158, and claim 8).
The teachings of Tang et al. and Brundage et al. are related because both are analyzing behaviors for products using neural networks. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the behavior analysis neural network model of Tang et al. to incorporate the target of Brundage et al. in order to specifically narrow comparisons between items.
Regarding Claims 2, 8, and 14: Tang et al. in view of Brundage et al. teach the limitations of claim 7. Tang et al. further teach wherein the to-be-trained model is further configured to: perform linear processing on the i.sup.th piece of the first information to obtain an i.sup.th piece of Q information, an i.sup.th piece of K information, and an i.sup.th piece of V information; and perform a first operation on the i.sup.th piece of Q information, N pieces of the K information, N pieces of the V information, and N pieces of weight information corresponding to the i.sup.th behavior to obtain an i.sup.th piece of second information, wherein a j.sup.th piece of the weight information corresponding to the i.sup.th behavior is determined based on the i.sup.th behavior and a j.sup.th behavior, and wherein j=1, . . . , N (See Figure 2, Paragraph 0004, Paragraphs 0070-0073, Paragraphs 0085-0088, Paragraphs 0099-0100, Paragraphs 0125-0130, Paragraph 0174, Paragraph 0177, and Paragraph 0183).
Regarding Claims 5, 11, 17, and 20: Tang et al. in view of Brundage et al. teach the limitations of claim 7. Tang et al. further teach wherein the to-be-trained model is further configured to: perform feature extraction on the N pieces of second information to obtain fifth information and sixth information, wherein the fifth information indicates a difference between the N behaviors, and wherein the sixth information indicates a same point between the N behaviors; obtain seventh information based on the fifth information and the sixth information, wherein the seventh information indicates interest distribution of the user; and calculate matching degrees between the seventh information and K pieces of eighth information, wherein the matching degrees are used as the predicted item recommendation result, wherein a t.sup.th piece of the eighth information indicates a t.sup.th second item, and wherein t=1, . . . , K (See Figure 2, Figures 4-5, Paragraph 0040, Paragraphs 0070-0073, Paragraphs 0085-0088, Paragraph 0092, Paragraph 0097, and claim 1).
Regarding Claims 6, 12, and 18: Tang et al. in view of Brundage et al. teach the limitations of claim 7. Tang et al. further teach wherein the K second items comprise N first items (See Figure 2, Figures 4-5, Paragraphs 0085-0088, and claim 1).
Regarding Claim 19: Tang et al. in view of Brundage et al. teach the limitations of claim 7. Tang et al. further teach wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform weighted summation on the fifth information and the sixth information to obtain the seventh information (See Figure 2, Paragraphs 0131-0136, and claim 2).
Allowable over 35 USC 103
Claims 3-4, 9-10, and 15-16 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Dependent claims 3-4, 9-10, and 15-16 disclose a system and method for item recommendation by obtaining user behaviors towards items by performing linear processing and weighting the pieces of data/information and categorizing them to obtain item recommendation results, then target specific items by analyzing the weightings and distances between the items using the specific mathematical relationships recited.
Regarding a possible 103 rejection: The closest prior art of record is:
Tang et al. (US 2020/0242450 A1) – which discloses behavior prediction methods and model training.
Brundage et al. (US 2008/0189225 A1) – which discloses use of neural networks to target specific items to users.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 3-4, 9-10, and 15-16, such as a system and method for item recommendation by obtaining user behaviors towards items by performing linear processing and weighting the pieces of data/information and categorizing them to obtain item recommendation results, then target specific items by analyzing the weightings a distances between the items using the specific mathematical relationships recited. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a method comprising: inputting N pieces of first information into a to-be-trained model to obtain a predicted item recommendation result, wherein the to-be-trained model is configured to: obtain the N pieces of first information, wherein an i.sup.th piece of the first information indicates an i.sup.th first item and an i.sup.th behavior, wherein the i.sup.th behavior is a behavior of a user for the i.sup.th first item, wherein N behaviors of the user correspond to M categories, and wherein i=1, . . . , N, N≥M, and M>1; process the N pieces of first information based on a multi-head self-attention mechanism to obtain N pieces of second information; obtain the predicted item recommendation result based on the N pieces of second information; determine, based on the predicted item recommendation result, a target item from K second items, wherein K≥1; and recommend the target item to the user; obtaining a target loss based on the predicted item recommendation result and a real item recommendation result, wherein the target loss indicates a difference between the predicted item recommendation result and the real item recommendation result; and obtaining a target model by updating a parameter of the to-be-trained model based on the target loss until a model training condition is met … perform linear processing on the i.sup.th piece of the first information to obtain an i.sup.th piece of Q information, an i.sup.th piece of K information, and an i.sup.th piece of V information; and perform a first operation on the i.sup.th piece of Q information, N pieces of the K information, N pieces of the V information, and N pieces of weight information corresponding to the i.sup.th behavior to obtain an i.sup.th piece of second information, wherein a j.sup.th piece of the weight information corresponding to the i.sup.th behavior is determined based on the i.sup.th behavior and a j.sup.th behavior, and wherein j=1, . . . , N … obtain N pieces of third information, wherein an i.sup.th piece of the third information indicates the i.sup.th behavior; perform a second operation on the i.sup.th piece of the third information and the N pieces of third information to obtain N pieces of fourth information corresponding to the i.sup.th behavior, wherein a j.sup.th piece of the fourth information corresponding to the i.sup.th behavior indicates a distance between the i.sup.th behavior and the j.sup.th behavior; and perform the first operation on the i.sup.th piece of Q information, the N pieces of the K information, the N pieces of the V information, the N pieces of the weight information, and the N pieces of the fourth information to obtain the i.sup.th piece of second information (as required by claims 3-4, 9-10, and 15-16)”, thus rendering claims 3-4, 9-10, and 15-16 as allowable over the prior art.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached 9-5 Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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